Validation of Early Human Dose Prediction: A Key ... - ACS Publications

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Validation of Early Human Dose Prediction: A Key Metric for Compound Progression in Drug Discovery Ken M. Page* Drug Safety and Metabolism, AstraZeneca, Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG, U.K. S Supporting Information *

ABSTRACT: Human dose prediction is increasingly recognized as an important parameter in Drug Discovery. Validation of a method using only in vitro and predicted parameters incorporated into a PK model was undertaken by predicting human dose and free Cmax for a number of marketed drugs and AZ Development compounds. Doses were compared to those most relevant to marketed drugs or to clinically administered doses of AZ compounds normalized either to predicted Cmin or Cmax values. Average (AFE) and absolute average (AAFE) folderror analysis showed that best predictions were obtained using a QSAR model as the source of Vss, with Fabs set to 1 for acids and 0.5 for all other ion classes; for clearance prediction no binding correction to the well stirred model (WSM) was used for bases, while it was set to Fup/Fup0.5 for all other ion classes. Using this combination of methods, predicted doses for 45 to 68% of the Cmin- and Cmax-normalized and marketed drug data sets were within 3-fold of the observed values, while 82 to 92% of these data sets were within 10-fold. This method for early human dose prediction is able to rank, identify, and flag risks or optimization opportunities for future development compounds within 10 days of first synthesis. KEYWORDS: human, clearance, volume of distribution, Vss, Vdss, Clint, dose prediction, pharmacokinetics, prediction, in vitro, in silico, protein binding

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INTRODUCTION The oral route is the most commonly used mode of delivering drugs to patients; it is easy for them to self-administer their medication without a hospital visit and enhances the chances of good compliance. Consequently, although other routes of administration remain important (intravenous, inhalation, intramuscular, etc.), the design of drugs capable of being administered orally remains the focus of the majority of small molecule drug discovery projects. As a consequence of this, the properties that cause a compound to be suboptimal for dosing by the oral route have been major causes of historical drug failure. This has been recognized for many years, probably since a review appeared that analyzed the reasons for drug failure during preclinical and clinical testing, concluding that poor drug metabolism and pharmacokinetic (DMPK) properties were major factors1 and was followed by other reviews of attrition up to the present day.2,3 From the end of the eighties to the present the majority (maybe all) of pharmaceutical companies has monitored and attempted to optimize an ever increasing number of these properties (for example, lipophilicity, protein binding, solubility, in vitro and in vivo clearance, plus bioavailability4−6). The huge advances in assay technology and capacity plus marked reductions in turnaround time for delivery of DMPK data have helped the evolution of this process enormously, as has the parallel improvements in data analysis tools and the science itself. As a consequence, it is now © 2015 American Chemical Society

possible to monitor a number of DMPK/physicochemical properties in a 5 to 10 day optimization cycle based on structure−property relationships (SPR) gleaned from relatively large data sets. With the generation of these data sets came the realization that the likely cause of clinical failure for a drug with suboptimal properties was due to a combination of all or many of them, usually manifesting as poor exposure in blood or plasma following an oral dose and by inference as poor penetration to the pharmacological target.7 As a result, a subtle but real change of approach has been evident, whereby the focus of optimization has changed to parallel (multiparameter) optimization, or MPO. This approach collates information from a number of disparate parameters into a summary metric that is used to show the progress a series has made toward a particular optimization goal following one or more rounds of optimization. One example of this is the prediction of a human dose that causes plasma or blood concentrations to exceed a certain level (usually based upon the potency of the compound against the pharmacological target for the project) for an agreed time period. The dose is predicted from a combination of properties that are measured during the Received: Revised: Accepted: Published: 609

November 6, 2015 December 18, 2015 December 23, 2015 December 23, 2015 DOI: 10.1021/acs.molpharmaceut.5b00840 Mol. Pharmaceutics 2016, 13, 609−620

Article

Molecular Pharmaceutics optimization cycle and can be of value in highlighting particular parameters that are suboptimal, through to defining a short-list of compounds to profile further to help selection for preclinical development. McGinnity et al.8 have validated an approach to human dose prediction (using a number of marketed drugs), which takes into account all parameters contributing to human pharmacokinetics (PK); clearance (CL), volume of distribution (Vss), protein binding, gut absorption/bioavailability, and dosing interval while assuming that efficacy will be observed if unbound plasma concentrations exceed an in vitro measure of potency against the pharmacological target over a defined period, and these authors demonstrated that they could predict clinically useful doses very accurately. The ambition of the work described here is to take the methodology further and produce an estimate for a human efficacious dose that is credible, either as a means of ranking compounds and highlighting optimization opportunities or as a reliable predictor of the exposure expected at a particular dose. Using an appropriate combination of data sources, this could be achieved as soon as possible after first synthesis of any compound. If this innovation proved to be acceptably accurate, potential candidate drugs (CDs) could be identified within days rather than weeks or months of first synthesis, improving compound quality while opening the way to better, faster decision making. Clearly, to achieve this ambition, we must use a method for predicting human dose that does not use in vivo data (for reasons of speed, volume of testing, ethical animal use, and often available compound quantity) and to deliver these estimates to a similar time scale as the optimization cycle. McGinnity et al.8 have approached this goal by mainly utilizing in vitro data and convincingly demonstrating the utility of clearance prediction from in vitro human metabolism data (an observation that replicates findings by other investigators9−22). However, their methodology also incorporates estimates for a number of parameters derived from in vivo data; part of the work outlined here is focused on the identification of techniques that could replace in vivo based data with that obtained largely from in vitro measurements, but also prediction or assumed values for Vss based on the properties of successful drugs. This article outlines the methodology used to determine human dose from in vitro data plus predicted Vss only and its validation using both the marketed drug data set used by McGinnity et al.8 and a set of AstraZeneca Development (AZD) compounds. The data and model used for human dose prediction can also be used to predict human free Cmax as a measure of exposure, and to further develop the usefulness of the method, these predictions have been compared to dosenormalized observed free Cmax values for a range of AZDs.

and plasma concentration observed 12 h after each dose) were available for each of them. All four ion classes are represented in this data set, while measured LogD values are in the range −0.22 to 5.90 and molecular weights are in the range 372 to 640 Da. A number of other parameters known to influence DMPK properties (PSA, NPSA, number of rotatable bonds, and number of Lipinski23,24 failures) have been calculated and are summarized in Supporting Information. A third data set was selected to evaluate the notion that a subset of a large number of synthetic designs could be selected for synthesis by use of a human dose prediction filter based entirely on either assumed parameter values (e.g., Fabs = 1) or in silico data. This comprised a large number of AZ compounds (ca. 500) and ensured that the four ion classes were well represented (including the AZD data set above); the various properties needed to predict human dose were themselves predicted for all compounds in the data set using in-house QSAR models. For these compounds, target IC50 was set to 0.01 μM (clearly in a real project setting measured potency would not be available for as yet unsynthesized targets, this is a common value that is considered to be acceptable and compounds would be given the best chance to succeed with this assumption), and human doses were predicted for each. For both marketed drug and AZ compound data sets in vitro Clint (intrinsic clearance) has been measured in either fresh preparations or cryopreserved batches of human hepatocytes. Likewise, LogD (pH7.4) and unbound fraction in human plasma (human Fup) values have been generated using in-house AZ methods. All data were collated using in-house AZ search tools, which also enable the calculation of the relatively complex terms such as those outlined in this article, giving ready access to these models to all discovery scientists whatever their location. The same search tool assigns an ion class to each compound according to structure and assigned a Vss value to each compound based on this. This tool also allows access to outputs from external QSAR models, a feature that enabled the use of a Vss value predicted by an in-house QSAR method based on the structure and measured human Vss data of drugs appearing in Obach et al.25 Human dose predictions were produced by assigning a dosing interval (12 h for AZD compounds, or based on a set of half-life criteria set by McGinnity et al.8 for the marketed drug set) and a minimum unbound plasma concentration that the model was set to exceed for the entire 24 h period at steady state. This was based on an in vitro IC50 measurement for the compound in a pharmacological assay deemed relevant to the target disease; for the AZD data set it was not adjusted by a multiplier, while for the marketed drug set this matched the potency value stated by McGinnity et al.8 and was often increased by a multiplier that was assumed necessary to achieve efficacy (for both data sets this was termed the free-fold IC50 target, or free Cmin). Both the source of Vss (QSAR or ion class) and Fabs (1 or 0.5; Fabs for most orally administered drugs is ≥0.5; by using both assumptions its effect on the accuracy of human dose predictions using this method could be evaluated) were varied as inputs to these models, while the method for predicting clearance from in vitro data was also varied for the basic AZD compound set. This resulted in four sets of predictions for the AZD data set based on common inputs for all ion classes. The combination of prediction methods that resulted in the best AFE/AAFE (AFE, average fold error; AAFE, absolute average fold error) data for each ion class was used to produce a final assessment for the AZD set, and this combination of

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DATA SETS USED FOR THE ANALYSIS Compounds used for the validation comprise two data sets: the first consists of 23 marketed drugs that appear in McGinnity et al.8 and includes at least one representative of all the major ion classes (acids, bases, neutrals, and Zwitterions). Measured LogD values for this data set are in the range −1.70 to 4.25 (from polar to very lipophilic examples); molecular weights are in the range 252 to 1203 Da. The second data set consists of 22 compounds selected by AZ to be administered by the oral route in human single ascending dose studies (AZ Development compounds or AZDs), chosen because the required PK parameters (maximum observed plasma concentration or Cmax 610

DOI: 10.1021/acs.molpharmaceut.5b00840 Mol. Pharmaceutics 2016, 13, 609−620

Article

Molecular Pharmaceutics

hepatocytes per incubation to reflect the total liver content per kg bodyweight, then applying the WSM13 (including an approximation that corrects for the degree of nonspecific binding in the in vitro incubation plus that observed in vivo), this parameter can be linked to in vivo clearance (eq 5):

approaches was then applied to the marketed drug data set. The finalized inputs were: QSAR Vss, Fabs = 1 (acids) or 0.5 (all other ion classes), WSM binding correction = 1 (bases), or = Fup/Fup0.5 (all other ion classes); Fup0.5 has been found to be a usable surrogate for the fraction unbound in the in vitro Clint incubation or Fuinc (unpublished data, analysis appears in Supporting Information). All of these parameters, plus potency, and the respective multipliers used for dose predictions are tabulated in Supporting Information.

CLscaled = Q h × Clint × SF × Fub/Fu inc

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(Q h + (Clint × SF × Fub/Fu inc))

MODEL AND PARAMETERS REQUIRED FOR HUMAN DOSE PREDICTION Human dose is predicted using eq 1, which relies on an estimate of free Cmin as an input and is designed to ensure that free exposure at steady state exceeds fold-corrected potency for the entire dosing interval:2,6, ⎡ MEC × AR × (k − k ) × Vss ⎤ ss a elim ⎥/F dose(mg kg −1) = ⎢ ⎢⎣ ⎥⎦ ka(e−kelimτ − e−kaτ )

(5)

where Qh = liver blood flow (20 mL min−1 kg−1 for humans;10,13 see above), SF = scaling factor (values used in this article were 0.96 for human microsomes and 2.88 for human hepatocytes, respectively), Fuinc = fraction unbound in the incubation; for the purposes of this work it was assumed that Fuinc ≡ Fu0.5 (for basic compounds, Fub/Fuinc was set to 1), and Fub = fraction unbound in human blood (assumed to be equivalent to Fup in plasma for this purpose). Finally, Cmax (maximum attained plasma concentration) can be calculated using eq 6 below:

(1)

where τ = dosing interval, MECss = minimum effective concentration (potency corrected for protein binding [human Fup in this case]), achieved at steady state; unit mg L−1.26 AR = accumulation ratio = 1 − e

−kelimτ

Cmax (μM) = coefficient × (elimination term 1000 − absorption term) × × Fu p MWt

(2)

kelim = elimination rate; clearance/Vss; unit h−1, and F = bioavailability = FabsEh.27 (3)

Qh = liver blood flow (20 mL min−1 kg−1 for humans10,13,28), F = bioavailability, and Fabs = fraction absorbed from the GI tract. ka = absorption rate, estimated from the equation (ln ka − ln kelim) = 1 hour ka − kelim

(6)

Where coefficient = Cmin·dose1/(e−kelimτ − e−kaτ), elimination term = e−kelimTmax/(1 − e−kelimτ), and absorption term = e−kaTmax/(1 − e−kaτ). Tmax, kelim, ka, and τ are described above. These equations are reproduced with permission from ref 31. Finally an estimate of human Vss is needed. As stated above two sources are obvious: the first assigns a generic value for Vss according to the ion class of each compound (acid, base, neutra,l or Zwitterion), and the second uses a QSAR model based on the human Vss data collated by Obach et al.25 Inspection of this data set gives median values for Vss of bases (2.9 L kg−1), acids (0.2 L kg−1), neutrals (1.3 L kg−1), and zwitterions (1.2 L kg−1); these values were used in the human dose calculations reliant on ion class Vss. The QSAR model predicts the logarithm of human Vss using measurements quoted in this data set consisting of compounds representing all four ion classes. The human Vss prediction explains 71% of the variance of the test set of 144 compounds and has an error in prediction of 0.39 log units. It performs better than a prediction of Vss based solely on charge type, which has an overall RMSE of ∼0.57.

Where E h = hepatic extraction ratio = 1 − (CL /Q h)

Tmax =

mL min−1 kg −1

(4)

The following assumptions are also made: efficacy is related to the free circulating concentration of drug, clearance is entirely or principally hepatic, and either absorption is complete or 50% of the orally administered dose. It then becomes clear that only the following four parameters are needed to estimate human dose using eq 1: clearance, Vss, potency, and the fraction unbound in human plasma (Fup), as stated in Grime et al.29 Of these parameters, potency is always measured immediately after synthesis as it is traditionally the focus of attention, while measurement of fraction unbound in plasma is relatively trivial or can be predicted with acceptable accuracy.30 Prediction of clearance from in vitro data has been the subject of many publications over the past decade, and it has been shown that it can be estimated with acceptable accuracy using microsomes prepared from liver tissue9−13 or hepatocytes isolated from human liver.12−19 Importantly, the introduction of cryopreservation techniques for human tissue in particular has revolutionized the ability of DMPK groups to routinely assess human clearance in hepatocytes.14,15,17−19 The parameter determined from incubation of compounds in these systems is intrinsic clearance (Clint, equivalent to Vmax/Km when substrate concentration is ≪Km), can be used to predict in vivo clearance and is derived from the relationship of the logarithm of parent amount remaining vs time. By first correcting the amount of microsomal protein or number of

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DATA ANALYSIS Predicted human doses were compared to either the most commonly used clinical dose (marketed drugs8) or the observed doses that were normalized by each of two methods (AZDs). 1. Cmin Normalized Doses. The free plasma concentration 12 h after dosing (free C12h, Cmin, or MECss) that was to be exceeded for the entire dosing period by the prediction was set to 1-fold IC50 for a particular compound. The dose of any compound administered to all individuals was then normalized: observed normalized dose = administered dose × C12h(prediction)/C12h(observed) 611


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Molecular Pharmaceutics 2. Cmax Normalized Doses. For the same compounds, the dose of a particular compound administered to all individuals was normalized: observed normalized dose = administered dose × Cmax(prediction) /Cmax(observed)

The median dose resulting from each of the normalization methods (AZDs) or the most commonly used clinical dose (marketed drugs) was compared to the corresponding predicted dose by calculating both average fold error (AFE) and absolute average fold error (AAFE) as follows:32 AFE = 10[∑ log10(prediction/observation)/number of observations]

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Figure 2. Distribution of molecular weights for marketed drug and AZD data sets.

AAFE = 10[∑ ABS(log10(prediction/observation))/number of observations]

RESULTS Characteristics of the Data Sets. Of the various physicochemical parameters calculated for the two data sets, only the three following showed an obvious difference between them: LogD, molecular weight, and distribution of ion class (the AZD data set having a greater fraction of compounds in the LogD 1 to 3 categories and with most compounds having molecular weights greater than 400). By contrast, the majority of the marketed drug data set had molecular weights ≤400 and LogD < 1 (Figures 1 and 2 for molecular weights and LogD,

Figure 3. Distribution of marketed drug and AZD data sets by ion class.

Fabs set to 1 or 0.5, using either QSAR or ion class-based Vss, with clearance predicted using the WSM with the binding correction Fup/Fuinc = Fup/Fup0.5, and are plotted in Figure 5, using predicted doses that include clearance predictions for bases with the binding correction to the WSM set to 1. AFE and AAFE data were calculated for the AZD data set as described for Figure 4, and findings are summarized in Table 1. AFE and AAFE data were calculated for the AZD data set as described for Figure 5, and findings are summarized in Table 2. Inspection of prediction outcomes for the AZD data set showed that both AFE and AAFE metrics were best when the QSAR prediction for Vss was used in the model, when Fabs was set to 1 for acids and 0.5 for all other ion classes, and where the binding correction used in the prediction of clearance was set to Fup/Fup0.5 for acids, neutrals, and zwitterions, and to 1 for bases. Figure 6 and Table 3 below show the relationship between predicted dose and Cmin-normalized clinical dose when doses were predicted in this way for the AZD data set. As this approach minimized prediction errors, it was applied both to the Cmax-normalized AZD data set (Figure 8 and Table 5 below) and to the marketed drug data set (Figure 7 and Table 4 below). Prediction outcomes for the AZD Cmin normalized and marketed drugs data set were very similar: between 82% of the dose predictions performed were within 10-fold of both the Cmin-normalized dose (AZDs) and for the most commonly administered dose (marketed drugs). Both AFE and AAFE data

Figure 1. Distribution of LogD ranges for marketed drug and AZD data sets.

respectively), in keeping with the conclusions of Leeson.33 In addition, around 50−60% of the marketed drug data set was bases, whereas a similar proportion of the AZD set was neutrals (Figure 3). Human Dose Predictions. This analysis presents the full validation conducted on the two data sets, a summary of which appears in ref 31. AZD Data Set. For human dose predictions associated with the AZD data set, dosing interval was set to 12 h and free exposure targets (IC50 values) were collated from documents available at transition to preclinical GLP studies; all measured data were obtained by querying in-house AZ databases and are available in Supporting Information. Dose predictions (mg dose−1 in this case) for this compound set are plotted vs the Cmin-normalized clinically administered doses in Figure 4 with 612


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Figure 4. Human dose predictions plotted vs Cmin-normalized clinical dose for AZD compounds applying Fabs = 1 or 0.5 and using QSAR Vss (A) or ion class Vss (B); clearance was predicted using the WSM incorporating the binding correction Fup/Fuinc = Fup/Fup0.5.

Figure 5. Human dose predictions plotted vs Cmin-normalized clinical dose for AZD compounds applying Fabs = 1 or 0.5 and using QSAR Vss (A) or ion class Vss (B); clearance was predicted using the binding correction to the WSM as shown in Figure 4 except for bases, where Fup/Fuinc = 1.

Table 1. Summary of AFE, AAFE, and Fold-Prediction Data for AZD Compound Dataset vs Cmin-Normalized Clinical Doses; Clearance Was Predicted As Shown In Figure 3 Vss source ion class QSAR model

Fabs

AFE

AAFE

% within 2-fold

% within 3-fold

% within 10-fold

% >10fold

% >10-fold UNDER predicted

% >10-fold OVER predicted

1.0 0.5 1.0 0.5

0.3 0.6 0.3 0.6

10.7 8.5 7.9 6.2

14 18 14 18

27 23 32 32

50 59 59 68

50 41 41 32

38 24 33 24

14 19 10 10

Table 2. Summary of AFE, AAFE, and Fold-Prediction Data for AZD Compound Dataset vs Cmin-Normalized Clinical Doses; Clearance Was Predicted As Shown In Figure 4 Vss source ion class QSAR model

Fabs

AFE

AAFE

% within 2fold

% within 3fold

% within 10fold

% >10fold



1.0 0.5 1.0 0.5

0.4 0.9 0.3 0.9

7.4 6.1 7.9 4.3

18 18 14 32

36 41 32 41

55 68 59 82

45 32 41 18

31 22 31 8

14 10 10 10

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Figure 6. Human dose predictions plotted vs Cmin-normalized clinical dose for AZD compounds applying Fabs = 1 (acids) or 0.5 (bases, neutrals, and Zwitterions), using QSAR Vss; clearance was predicted using the binding correction to the WSM as shown in Figure 5 except for bases, where Fup/Fuinc = 1.

Figure 7. Human dose predictions plotted vs most commonly administered dose for marketed drugs, applying Fabs = 1 (acids) or 0.5 (bases, neutrals, and Zwitterions), using QSAR Vss; clearance was predicted using the binding correction Fup/Fup0.5 to the WSM for acids, neutrals, and wzitterions and was set to 1 for bases.

for the two compound sets were also broadly similar, although systematic bias was slightly more pronounced for marketed drugs compared to AZDs (AFE = 0.6 and 0.8 for the two groups, respectively). Overall error differed slightly between the two data sets, too (AAFE values were 3.3 and 4.0 for marketed drugs and AZDs, respectively). While the distribution of predictions within 2-, 3-, and 10-fold were broadly similar, a larger fraction were within 3-fold (55%) for the marketed drug data set, while for the AZD data set those for 45% of compounds were within 3-fold. Finally, for the remaining 18% of both data sets whose dose predictions were not within 10fold of the observed dose, all of those for the marketed drug data set were under-predictions, while 8 and 10% of those for the AZD data set were under and over predicted, respectively. Comparison of Observed Clinical vs Predicted Free Cmax. Although there are many parameters that can be used to assess toxicological outcomes, for a number (with particular emphasis on activity at the hERG ion channel34−36) the magnitude of free exposure (either directly related to, or correlated with, Cmax) is most commonly used. Consequently, an early estimate of human free Cmax will allow the IC50 generated for such a target to be put into context rapidly, and for this reason, the human dose model used in this work incorporates such an estimate. To assess whether predicted values are in any way accurate and therefore potentially useful, Cmax data was obtained from the 22 compounds of the AZD data set that have been administered to volunteers and/or patients in the clinic and compared to predicted free Cmax data. Dose predictions were undertaken using the best case input parameters, i.e., Fabs = 0.5 for bases, neutrals, and Zwitterions and Fabs = 1 for acids; the Vss prediction from our QSAR model was used, and the binding correction incorporated into the WSM was set to Fup/Fup0.5 for acids, neutrals, and Zwitterions and to 1 for basic compounds. These were

compared to the observed data normalized; in all cases, observed Cmax data was obtained across a range of doses. Each was multiplied by the human Fup value known for that compound to give a free Cmax value at each dose, then normalized to the dose predicted by the method outlined here. The median of the normalized doses was then calculated (to prevent outliers skewing this data, perhaps caused by nonlinearity in PK due to saturation of absorption at higher doses), which was then used for comparison. It should be noted that, in order to predict exposure (i.e., free Cmax), dose is varied until free Cmin matches the exposure target (i.e., IC50). However, this predicted exposure will not change as Fabs is varied, but the dose required to achieve this exposure will (assuming linear pharmacokinetics). By contrast, actual observed exposure will vary in direct proportion to the dose administered (making the same assumption). Predicted vs Cmaxnormalized dose data are plotted below in Figure 8, with statistics summarized in Table 5. As can been seen, the dose at which a particular free Cmax is attained is predicted within 2- and 3-fold for 50 and 68% of the AZD data set, respectively; furthermore, 91% of the data set was predicted within 10-fold. Systematic error was rather low, with AFE = 0.8, and overall fold-accuracy was also good (AAFE = 2.8).

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DISCUSSION There are a number of multiparametric approaches in the literature that allow ranking of compounds according to a mix of properties,4,37−41 and this breadth of choice highlights the usefulness of these methods. Prediction of human dose as outlined here is another example alongside these. Early human dose prediction can be used effectively as a scoring metric, and it offers a further substantial advantage by providing predictions of additional relevant clinical parameters (free Cmax and half-

Table 3. Summary of AFE, AAFE, and Fold-Prediction Data vs Cmin Normalized Clinical Doses for AZD Compounds; Clearance Was Predicted As Shown for Figure 5 Vss source

Fabs

AFE

AAFE

% within 2fold

% within 3fold

% within 10fold

% >10fold



QSAR model

0.5/1.0

0.8

4.0

32

45

82

18

10

8

614


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Table 4. Summary of AFE, AAFE, and Fold-Prediction Data vs Most Commonly Administered Dose for Marketed Drugs; Clearance Was Predicted As Shown for Figure 6 Vss source

Fabs

AFE

AAFE

% within 2fold

% within 3fold

% within 10fold

% > 10fold

% > 10-fold UNDER predicted

% > 10-fold OVER predicted

QSAR model

0.5/1.0

0.6

3.3

45

55

82

18

18

0

of human Vss, this work has shown that human dose can be predicted to within 3- to 5-fold of either the most commonly recorded clinical dose (marketed drugs) or actual clinical doses (AZD compounds) over a very diverse range of chemistry. Because this prediction is based purely on in vitro and in silico data, it can be formulated within very few days of first synthesizing the compound (dependent on the delivery time of in vitro data). Furthermore, where the project team has reason to have confidence in this data (around 50% of all predictions were within 3-fold of the quoted figure), this prediction has the potential for highlighting compounds that are likely to have the combination of human pharmacokinetics and potency that makes them candidates for preclinical development. A similar degree of prediction accuracy was observed across the marketed drug and AZD compound data sets despite the physicochemical differences between the two, suggesting that this approach is likely to be of value whatever the properties of particular series. Of the parameters that are required to predict human dose, potency and fraction unbound in human plasma are usually measured and can be used directly, while clearance must be predicted from the measured Clint data. Half-life is key to the prediction of human dose using this model, which assumes that duration of exposure above a potency target will result in efficacy, and clearance is one of the two parameters (the other being Vss) that drive half-life.29 Clearly the method used for predicting clearance is pivotal to the success of the whole approach. The WSM has proven itself to be the most useful of the various hepatic flow models used,12 although there is a body of evidence that suggests the fraction of drug that is not subject to nonspecific binding in the incubation (fraction unbound in the incubation; Fuinc) must be incorporated into the model and that prediction of clearance needs to be further refined.41−44 Incorporation of Fuinc using the binding correction Fup/ Fuinc19,40−43 can be achieved in three ways: by measurement, by use of a predictive model, or by use of a surrogate. Measurement is clearly the best of the various options, but would involve running another assay, potentially on all synthetic output of LO projects across the business. Predictive models available in the literature42−44 require LogP to be measured or need further predicted parameters (such as cLogP) to be included in the simulation. Consequently, to simplify the amount of measured or predicted data needed as inputs to the model, this work relies on the final option (use of a surrogate for Fuinc), which in this case uses the ratio Fup/ Fup0.5 (acids, neutrals, and Zwitterions), or sets the ratio Fup/ Fuinc to 1 (bases). This requires no extra measurement or parameter prediction, while comparison of predicted clearance based on in-house data available for compounds quoted in Sohlenius-Sternbeck et al.19 has demonstrated that the two

Figure 8. Human dose predictions plotted vs Cmax-normalized clinical dose for AZD compounds applying Fabs = 1 (acids) or 0.5 (bases, neutrals, and Zwitterions), using QSAR Vss; clearance was predicted using the binding correction to the WSM as shown in Figure 7.

life) in addition to a clinical dose estimate. Such data is intuitive and easy to understand for all members of a discovery design team and can be used directly in compound design and progression. On occasion it allows compounds with suboptimal potency to be rescued by favorable PK or can rank compounds clearly with very disparate properties that often combine to give similar human doses or exposure. It can further put the commonly measured in vitro solubility of compounds into context across a series or project (or all discovery projects), by comparing predicted dose to the Curatolo40 maximum absorbable dose (cMAD; an approximation of the maximum absorbable dose using an assumed permeability and measured solubility). Estimation of free Cmax values allows a relevant and simple risk assessment of in vitro safety signals in the context of (predicted) clinical data. Application of the method can on occasion rescue compounds that appear quite potent against in vitro toxicity end-points. For example, an in vitro-derived hERG IC50 measure can be viewed in the context of likely human exposure (Cmax) and a high Cmax to hERG IC50 ratio, despite a relatively potent effect on the hERG channel, may then appear less risky than when using the hERG IC50 in isolation. For these reasons, prediction of human dose prior to entry of a candidate drug into preclinical GLP studies is an undertaking that has grown in popularity, to indicate whether risks exist that are related to human pharmacokinetics and efficacy. By making some simple assumptions, either for parameters difficult to predict (e.g., magnitude of Fabs) or that clearance is predictable from in vitro measurements, and using a prediction

Table 5. Summary of AFE, AAFE, and Fold-Prediction Data vs Cmax-Normalized Clinical Doses for AZD Compounds; Clearance Was Predicted As Shown for Figure 5 Vss source

Fabs

AFE

AAFE

% within 2fold

% within 3fold

% within 10fold

% >10fold



QSAR model

0.5/1.0

0.8

2.8

50

68

91

9

5

5

615


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With the exception that predictions for marketed drugs are underestimates if not within 10-fold, whereas when there is more than a 10-fold difference in prediction for the Cminnormalized AZD data set an overestimate is as likely as under, there is a similar spread of prediction accuracy across the two data sets. Awareness of under-prediction is important, as under these circumstances compounds will be progressed to further testing, and as a consequence, there is a low chance that they will be ignored. However, if the prediction used to choose compounds for progression is both inaccurate and pessimistic, there is a real risk that compounds of promise will be missed; a bad outcome for a design team. Reflecting on the performance of the model, although the source of Vss appears relatively unimportant, using the QSAR output to set Vss, by setting Fabs to 1 for acids and to 0.5 for all other ion classes, and finally setting the WSM binding correction to 1 for bases and Fup/ Fup0.5 for all others gives the best mix of minimized systematic (AFE) and fold-error (AAFE) for the AZD (Cmin and Cmax normalized) and marketed drug data sets. Inspection of the free human Cmax data that was generated for AZD compounds as an output of the prediction model has shown that this parameter has a better degree of prediction accuracy, is therefore a useful guide to potential toxicity concerns associated with the magnitude of free plasma exposure and suggests that the prediction of human pharmacokinetics by this method is good. Provided that this approach for human dose prediction is used with care there should be little risk of rejecting compounds inappropriately. The predictions will demonstrate themselves to be acceptably accurate, or if not, either they will be more optimistic than those based on more complete data in which case unsuitable compounds will be rejected by the generation of further data, or if these early predictions are generally pessimistic for a particular series then they will come to be used only to rank compounds for further testing, and the worst outcome is that further characterization for particular compounds is delayed by a week or two. Although the absolute fold error is relatively high (particularly for the Cmin-normalized AZD data set; AAFE = 4.0) Figure 9 below, which replicates the data shown in Figure 6 but includes a dose benchmark set to 500 mg (considered to be the maximum dose that can realistically assess an orally

approaches are similar comparing predicted to observed hepatic CL for this data set (analysis in Supporting Information; AAFE values = 1.0 and 1.6 and AFE values = 0.7 and 1.0 for Fuinc surrogate and regression corrected Fuinc approaches, respectively). It should be noted that this surrogate-based correction method is a rather crude substitution for Fuinc coupled to the regression approach. Nevertheless, it seems fit for purpose as a rapid, pragmatic method to correct for binding when using the WSM for prediction of clearance from Clint data. As Vss is the second of the two parameters involved in the estimation of half-life, then a value for each compound must be estimated. The central aspiration described here is to use only in vitro data; consequently the usual approaches utilizing preclinical in vivo data6,8,22 are not available, and Vss must therefore be obtained using physicochemical properties as a starting point. As a result, the modeling presented here uses Vss estimated either from a QSAR model based on the data collated by Obach et al.25 or from the median values from this data for each of the ion classes in the same data set. Although predictions based on the QSAR model of Vss tend to be somewhat superior to those using Vss based on ion class, there is little practical difference between the two. The final parameter needed to generate a prediction is the fraction of a dose that is absorbed following oral administration (Fabs). Absorption is often a key uncertainty for compounds that enter development, but it can be stated with certainty that if any compound is to be a successful oral drug then it is likely that the real in vivo absorption of the compound will be ≥50%. Consequently, if it is assumed that oral absorption is complete (i.e., Fabs = 1) or 50% (Fabs = 0.5), the absorption of those compounds with potential is unlikely to be exaggerated, while the remainder will be filtered, either by poor in vivo absorption later in the testing cascade, or by one of the many other pieces of adverse data generated for most synthetic targets. Prediction of free Cmax can be viewed as a “pure” PK prediction, rather than an attempt to predict efficacious dose or exposure. By definition it is the maximal exposure achieved at a particular dose, and even if the predicted dose itself is only accurate to within 3-fold, the predicted free Cmax will still be attained at some realistic point in a clinical dose escalation study. Consequently, acceptable predictions of free Cmax allow a judgment to be made of risks such as hERG activity associated with free Cmax at doses likely to be administered in the clinic. If the risk appears high, then a particular compound can be assumed to be an unlikely clinical candidate and deprioritized in the testing cascade. Furthermore, if this risk is common among members of a series, then this represents clear guidance as to a key issue that should be a focus of optimization. To compare the output from this model with known clinical doses or dose predictions for AZDs, two parameters were used. The first is a useful parameter that can be used to show whether a set of predictions systematically under or overpredict observed data and is average fold error (AFE); in this case AFE ≪ or ≫ 1, respectively. However, AFE gives little information around the overall magnitude of error; a value of 1 does not necessarily imply a perfect prediction; it is instead possible that the various under and overprediction errors are evenly distributed around the line of unity, canceling each other. Absolute average fold error (AAFE), however, shows the average magnitude of all prediction errors. Consequently, both methods were used to compare predicted doses with either those used clinically or those predicted for AZD compounds on transition into preclinical development.

Figure 9. Human dose predictions plotted vs Cmin-normalized dose for AZD compounds applying Fabs = 1 (acids) or 0.5 (bases, neutrals, and Zwitterions), using QSAR Vss; clearance was predicted using the binding correction to the WSM as shown in Figure 7; 500 mg dose benchmark added (Figure reproduced with permission from ref 31). 616


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Figure 10. Human dose predictions vs arbitrary sequential synthesis weeks for a project consisting of a neutral series: (A) no filtering; (B) filtered by predicted dose (10 μM); (C) filtered further by hERG IC50 to free Cmax ratio; (D) further filtered by cMAD to dose ratio (markers have been jittered in Spotfire).

administered drug in the clinic),31 shows that this need not be an impediment to applying this data for good decision making. This shows that 18 of 22 AZDs were flagged as true positives using this model, i.e., both predicted and corrected clinically administered doses were below the 500 mg benchmark. Of the remaining four, two were flagged as false positives, that is the dose at which the desired Cmin was predicted to be achieved was lower than that observed clinically (these compounds would pass this hurdle, would be tested further, and would not be ignored at this stage), one was flagged as a true negative (both predicted and observed doses exceeded the benchmark), while the last was flagged as a false negative (observed dose was below the benchmark but the prediction exceeded it). Consequently the dose for 20 of 22 compounds was predicted sufficiently well to allow them to be flagged as potential CDs and to allow a project team to test them further (around a 90% success rate). Comparing the Cmax-normalized AZD data set gave an almost identical outcome (data not shown). As a further example of the utility of this approach, data for an AZ Discovery project (neutral series) is shown in Figure 10, plotting the dose predictions calculated in any arbitrary synthetic week over the lifetime of the project. It is noteworthy that all shortlist compounds and CDs were flagged by the early human dose prediction as potential CDs in this project, while few if any of the compounds thought worthy

of further investigation (rodent PK, etc.) by the project at the time would have been ignored; moreover, other compounds might have been considered based on their dose predictions that were not investigated by the project. Furthermore, consideration of free Cmax data in addition to dose predictions can give a more rigorous analysis than is usually possible so soon after first synthesis. Panel A shows all compounds over a 13 week synthesis period, and predicted human doses range from >40000 mg to ca. 1 mg. Compounds can be filtered immediately according to all measured and calculated parameters that are important to the design team, including predicted dose, hERG IC50, hERG IC50 to free Cmax ratio, and cMAD to dose ratio (see panels B to D). Whether prior to LO or later in discovery, this early estimate of human dose enables a design team to see clearly on a weekly basis which fraction of compounds has the potential to become CDs and those that do not, and further to identify the key properties that need to be addressed for the next round of chemistry. Finally, it would clearly be of great value to be able to predict human doses for compounds that are yet to be first synthesized, to enable a project team to focus only on those with a realistic chance of becoming CDs; by its nature, such a prediction must rely entirely on in silico data as inputs to the model. Relatively accurate predictive models are routinely available for parameters such as fraction unbound in human plasma. 617


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Figure 11. Predicted human doses using in silico input data including the AZD validation data set and other CDs.

used with great care, and at worst, it seems that its use must wait until prediction technologies are better able to cope with complex processes such as in vitro or in vivo clearance. In summary, we propose a simplified model for clinical dose prediction and show its use as a decision-making tool in the context of a drug-design project using two sets of data, one comprising approved drugs and another consisting of late-stage candidate-drug compounds at AstraZeneca. Both data sets for AZ CDs and for marketed drugs were used to validate the approach, which has now been successfully integrated and embedded as best practice for discovery projects within our laboratories. The data sets were used to gauge the effect that accuracy of input parameters has on predictions. Analysis shows clearly that the source of Vss in the model makes little difference to the outcome. However, the assumption that human absorption is 50% (Fabs = 0.5) for all ion classes except acids (Fabs = 1) result in somewhat lower fold errors than assuming that absorption is complete. A good estimate of clearance is crucial, and results suggest that commonly used in vitro assay results can be scaled reliably to reflect expected in vivo clearance for drug candidates with a typical drug-like profile (reasonable lipophilicity, molecular weight, and other properties as advocated by Leeson et al.33). However, especially for series whose physicochemical properties lie outside this range, there is a risk that clearance is dominated by nonmetabolic processes, that it will cause total clearance to be underpredicted using in vitro data and consequently doses based on these estimates will also be optimistic. In these cases, it is always good practice to build an extra component of clearance into the prediction model to ensure that only the best compounds are progressed. The model that assumes clearance is predominantly metabolic can always be revisited at a later date and used to highlight historical compounds that may be of value if clearance for some reason is then considered to be predominantly metabolic in nature.

Furthermore, large pharmaceutical companies have Clint data on tens of thousands of compounds that might make it possible for QSAR models to predict Clint in hepatocytes or microsomal systems to be built (such models are available to AstraZeneca scientists), and we already know that there are methods for predicting human Vss. Consequently, all parameters required to make a prediction of human pharmacokinetics are potentially available, with the exception of a target Cmin concentration. To complete the model we need such a value, and for this example, one was chosen arbitrarily (many projects target an IC50 value for their primary pharmacological potency of ≤0.01 μM, so this seems an appropriate choice). We have already seen that the prediction of clearance is very often crucial to establishing a useful prediction of human dose; consequently this in silico approach will be very reliant on the quality of the predictive Clint model used. Predicted data and ultimately human doses assuming a 12 h dosing interval and free Cmin = 0.01 μM were generated for ca. 570 AZ compounds with at least 90 examples of each of the four ion classes present. This data set included the 22 AZDs that were used in the original validation above. Predicted dose is plotted in Figure 11. The benchmarks are set to the median of the whole acid, base, neutral, or Zwitterion data sets, rounded to the nearest 10 mg dose. Half of the data set for each ion class is therefore above and half below the relevant benchmark and could be used to select which synthetic targets to make. However, this risks missing potential CDs: although only 1 of 5 acidic AZDs would be missed and the single Zwitterionic CD would have been chosen for synthesis, 5 of 7 neutral and 5 of 8 basic AZDs would not have been made following these rules. Furthermore, by applying this benchmark we would only have reduced the choice of compounds to be made by 50%; between the loss of AZDs and the ability to remove only 50% of ideas using this model, this does not represent an efficient filtering step! It is possible that the choice of a target IC50 value across the whole range of compounds was not a good one and that it would be better to choose on a project by project and maybe even a weekly basis, so that experience can be incorporated into this model (or if possible predicted IC50 values from a QSAR model could be used). However, given the sensitivity of the model to half-life and clearance in particular,29 it is more likely that inaccuracy in the prediction of in vitro Clint is the predominant factor here. Consequently, at best this in silico model should be

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CONCLUSIONS Overall, human dose prediction with acceptable accuracy, based only on a mixture of in vitro and predicted human (Vss) data, is readily achievable and has been shown to have real potential in producing credible dose estimates, thereby identifying potential CDs after one round of testing. It has also been shown to be a useful tool for ranking compounds in lead optimization using both likely efficacious dose and risks associated with free Cmax. 618


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Molecular Pharmaceutics As the predicted human dose and pharmacokinetics rely entirely on a mixture of in vitro and predicted human Vss data, they can be applied within a week or two of first synthesis to allow rapid assessment of compounds with potential to become candidate drugs. The model was validated against the AZD data set by changing the Fabs input between 0.5 and 1 while also varying the binding correction to the WSM so that Fup/Fuinc was set either to 1 or Fup/Fup0.5 and finally setting Vss to either the output of an in-house QSAR model or median Vss value by ion class, both based on the Obach data set. The outcome of this was that best predictions resulted from setting Fabs to 0.5 for bases, neutrals, and Zwitterions and 1 for acids, from setting the WSM binding correction to 1 for bases and Fup/Fup0.5 for the remainder and finally by using the QSAR model for Vss. However, using median Vss values based on ion class resulted in only slightly inferior predictivity. Consequently, using median Vss based on ion class is a perfectly valid approach, making this method accessible to any team able to generate in vitro human Clint and Fup data. Prediction of free Cmax in human compared to observed data has shown it to have equivalent, if not superior, accuracy to that for free Cmin. Predicted Cmax provides the designer with a context for assessing the results from safety assays, enabling an informed assessment of the risk associated with these readouts. Clearly knowledge of many of these risks would be advantageous at an early stage in the life of any compound, so the question of how early that can be appears to be answered: just as soon and as fast as the project can generate the appropriate in vitro data; but generating these predictions based wholly on in silico data must wait for the availability of better models, particularly for the prediction of Clint. We surmise that, looking at the life span of a project from early lead-generation to nomination of a candidate drug, human dose and Cmax predictions would become important in late leadgeneration, when designers focus on a number of series of chemical equity but do not yet have a clear view, either of their respective issues or inherent quality. At that stage predictions of clinical dose can serve as a first measure of quality and highlight the front-runner compounds in each series. Additionally, Cmax predictions provide a context to in vitro safety data for the best compounds, thereby guiding design toward better compounds by enabling the most important issues to be identified. Finally, for compounds or series whose clinical dose predictions are inadequate for progression, these values can still highlight those parameters that are key to the poor prediction: high clearance (how much lower would result in an adequate dose?), poor potency (how much more potency is needed?), protein binding, or Vss.

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used clinical dose (marketed drugs) used for comparison. Table 3 compares clearance data, predicted using measured data described by Sohlenius-Sternbeck et al.19 and the modified WSM described in this article, to predicted clearance data generated from the same data reported by the authors (XLS)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +44 7894 810117. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Drs. Peter Ballard and Willem Nissink are acknowledged for their very helpful input into the preparation of this article.

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ABBREVIATIONS AAFE, absolute average fold error; AFE, average fold error; AZ, AstraZeneca; AZD(s), AstraZeneca Development compound(s); CD(s), candidate drug(s); CL, clearance; Clint, intrinsic clearance; Cmax, maximum observed concentration in plasma; Cmin, (minimum) plasma concentration observed just prior to the next dose; Da, Daltons; Fabs, fraction of an oral dose absorbed from the gastrointestinal tract; Fub, Fraction unbound in blood; Fuinc, Fraction unbound in the microsomal or hepatocyte incubation; Fup, fraction unbound in plasma; LO, lead optimization; LogD, logarithm of the ratio of the sum of the concentrations of all forms of the compound (ionized plus unionized) in each of the lipophilic (octanol) and aqueous phases; MPO, multiple parallel optimization; SF, scaling factor, used to normalize milligrams opf microsomal protein or number of hepatocytes per incubation to kilograms of bodyweight; SPR, structure−property relationship; Vmax, maximum rate achieved by a system at saturating substrate concentrations; Vss, volume of distribution at steady state

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REFERENCES

(1) Prentis, R. A.; Lis, Y.; Walker, S. R. Pharmaceutical Innovation by the seven UK-owned pharmaceutical companies (1964−1985). Br. J. Clin. Pharmacol. 1987, 25, 387−396. (2) Kola, I.; Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discovery 2004, 3, 711−718. (3) Summerfield, S.; Jeffrey, P. Discovery DMPK: changing paradigms in the eighties, nineties and noughties. Expert Opin. Drug Discovery 2009, 4, 207−218. (4) Smith, D. A.; Jones, B. C.; Walker, D. Design of Drugs Involving the Concepts and Theories of Drug Metabolism and Pharmacokinetics. Med. Res. Rev. 1996, 16 (3), 243−266. (5) Eddershaw, P. J.; Beresford, A. P.; Bayliss, M. K. ADME/PK as part of a rational approach to drug discovery. Drug Discovery Today 2000, 5 (9), 409−414. (6) Riley, R. J.; Martin, I. J.; Cooper, A. E. The influence of DMPK as an integrated partner in modern drug discovery. Curr. Drug Metab. 2002, 3, 527−550. (7) Beaumont, K.; Smith, D. A. Does human pharmacokinetic prediction add significant value to compound selection in drug discovery research? Curr. Op. Drug Disc. Dev. 2009, 12 (1), 61−71. (8) McGinnity, D. F.; Collington, J.; Austin, R. P.; Riley, R. J. Evaluation of human pharmacokinetics, therapeutic dose and exposure predictions using marketed oral drugs. Curr. Drug Metab. 2007, 8, 463−479.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.molpharmaceut.5b00840. Additional tables, one collating calculated and measured physicochemical properties of all compounds in both the AZD and marketed drug data sets. The second table tabulates measured and predicted data used as inputs to the dose prediction model. Also included are all individual dose and PK parameter predictions plus the Cmin- and Cmax-normalized clinical doses (AZDs) or most 619


Article

Molecular Pharmaceutics (9) Houston, J. B. Utility Of In vitro Drug Metabolism Data In Predicting In vivo Metabolic Clearance. Biochem. Pharmacol. 1994, 47, 1469−1479. (10) Obach, R. S. Prediction of human clearance of twenty nine drugs from hepatic microsomal intrinsic clearance data: an investigation of in vitro half-life approach and non-specific binding to microsomes. Drug Metab. Dispos. 1999, 27 (11), 1350−1359. (11) Naritomi, Y.; Terashita, S.; Kimura, S.; Suzuki, A.; Kagayama, A.; Sugiyama, Y. Prediction Of Human Hepatic Clearance From In vivo Animal Experiments And In vitro Metabolic Studies With Liver Microsomes From Animals And Humans. Drug Metab. Dispos. 2001, 29, 1316−1324. (12) Ito, K.; Houston, J. B. Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm. Res. 2004, 21, 785−792. (13) Riley, R. J.; McGinnity, D. F.; Austin, R. P. A Unified Model For Predicting Human Hepatic, Metabolic Clearance From In vitro Intrinsic Clearance Data In Hepatocytes and Microsomes. Drug Metab. Dispos. 2005, 33, 1304−1311. (14) Li, A. P.; Brent, J. A.; Pham, C.; Fackett, A.; Ruegg, C. E.; Silber, P. M. Cryopreserved human hepatocytes: characterization of drugmetabolizing enzyme activities and applications in higher throughput screening assays for hepatotoxicity, metabolic stability, and drug−drug interaction potential. Chem.-Biol. Interact. 1999, 161, 17−35. (15) McGinnity, D. F.; Soars, M. G.; Urbanowicz, R. A. Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance. Drug Metab. Dispos. 2004, 32 (11), 1247−1253. (16) Naritomi, Y.; Terashita, S.; Kagayama, A.; Sugiyama, Y. Utility Of Hepatocytes In Predicting Drug Metabolism: Comparison Of Hepatic Intrinsic Clearance In Rats and Humans In vivo And In vitro. Drug Metab. Dispos. 2003, 31, 580−588. (17) Shibata, Y.; Takahashi, H.; Chiba, M.; Ishii, Y. Prediction of hepatic clearance and availability by cryopreserved human hepatocytes: an application of serum incubation method. Drug Metab. Dispos. 2002, 30, 892−896. (18) Soars, M. G.; McGinnity, D. F.; Grime, K.; Riley, R. J. The Pivotal Role of Hepatocytes in Drug Discovery. Chem.-Biol. Interact. 2007, 168, 2−15. (19) Sohlenius-Sternbeck, A.-K.; Afzelius, L.; Prussis, P.; Neelissen, J.; Hoogstraate, J.; Johansson, J.; Sternbeck, J.; Petersson, C. Evaluation of the human prediction of clearance from hepatocyte and microsome intrinsic clearance for 52 drug compounds. Xenobiotica 2010, 40 (9), 637−649. (20) Ito, K.; Houston, B. J. Prediction of Human Drug Clearance from in vitro and Preclinical Data Using Physiologically Based and Empirical Approaches. Pharm. Res. 2005, 22, 103−110. (21) Houston, J. B.; Galetin, A. Methods for predicting in vivo pharmacokinetics using data from in vitro assays. Curr. Drug Metab. 2008, 9, 940−951. (22) Hosea, N. A.; Collard, W. T.; Cole, S.; Maurer, T. S.; Fang, R. X.; Jones, H.; Kakar, S. M.; Nakai, Y.; Smith, B. J.; Webster, R.; Beaumont, K. Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches. J. Clin. Pharmacol. 2009, 49, 513−533. (23) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 1997, 23, 3−25. (24) Lipinski, C. A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technol. 2004, 1, 337−341. (25) Obach, R. S.; Lombardo, F.; Waters, N. J. Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds. Drug Metab. Dispos. 2008, 36, 1385−1405. (26) Rowland, M.; Tozer, T. N. In Clinical Pharmacokinetics Concepts and Applications, 3rd ed.; Lea & Febiger: Philadelphia, PA, 1998; p 492. (27) Rowland, M.; Tozer, T. N. In Clinical Pharmacokinetics Concepts and Applications, 3rd ed.; Lea & Febiger: Philadelphia, PA, 1998; p 84.

(28) Rowland, M.; Tozer, T. N. In Clinical Pharmacokinetics Concepts and Applications, 3rd ed.; Lea & Febiger: Philadelphia, PA, 1998; p 531. (29) Grime, K. H.; Barton, P.; McGinnity, D. Application of In Silico, In Vitro and Preclinical Pharmacokinetic Data for the Effective and Efficient Prediction of Human Pharmacokinetics. Mol. Pharmaceutics 2013, 10, 1191−1206. (30) Khan, M. T. H. Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches. Curr. Drug Metab. 2010, 11, 285−295. (31) Nissink, W.; Degorce, S.; Page, K. Lead Generation. Volume 67: Methods and Strategies (Methods and Principles in Medicinal Chemistry); Holenz, J., Ed.; in press. (32) Tang, H.; Hussain, A.; Leal, M.; Mayersohn, M.; Fluhler, E. Interspecies Prediction of Human Clearance Based on Scaling Data from One or Two Species. Drug Metab. Dispos. 2007, 35 (10), 1886− 1893. (33) Leeson, P. D.; Springthorpe, B. The Influence of drug-like concepts on decision-making in medicinal chemistry. Nat. Rev. Drug Discovery 2007, 6 (11), 881−890. (34) FDA guidance S7B Nonclinical Evaluation of the Potential for Delayed Ventricular Repolarization (QT Interval Prolongation) by Human Pharmaceuticals, 2005. (35) Roden, D. M. Drug-Induced Prolongation of the QT Interval. N. Engl. J. Med. 2004, 350, 1013−1022. (36) Bowlby, M. R.; Peri, R.; Zhang, H.; Dunlop, J. hERG (KCNH2 or Kv11.1)K+ channels: screening for cardiac arrhythmia risk. Curr. Drug Metab. 2008, 9, 965−970. (37) Gleeson, M. P. Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem. 2008, 51, 817−834. (38) Braggio, S.; Montanari, D.; Rossi, T.; Ratti, E. Drug efficiency: a new concept to guide lead optimization programs towards the selection of better clinical candidates. Expert Opin. Drug Discovery 2010, 5, 609−618. (39) Johnson, T. W.; Dress, K. R.; Edwards, M. Using the golden triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett. 2009, 19, 5560−5564. (40) Curatolo, W. Physical chemical properties of oral drug candidates in the discovery and exploratory development settings. Pharm. Sci. Technol. Today 1998, 1, 387−393. (41) Chang, G.; Steyn, S. J.; Umland, J. P.; Scott, D. O. Strategic Use of Plasma and Microsome Binding To Exploit in vitro Clearance in Early Drug Discovery. ACS Med. Chem. Lett. 2010, 1, 50−53. (42) Austin, R. P.; Barton, P.; Cockcroft, S. L.; Wenlock, M. C.; Riley, R. J. The Influence of nonspecific microsomal binding on apparent intrinsic clearance and its prediction from physicochemical properties. Drug Metab. Dispos. 2002, 30, 1497−1503. (43) Austin, R. P.; Barton, P.; Mohmed, S.; Riley, R. J. The binding of drugs to hepatocytes and its relationship to physicochemical properties. Drug Metab. Dispos. 2005, 33, 419−425. (44) Gertz, M.; Kilford, P. J.; Houston, J. B.; Galetin, A. Drug lipophilicity and microsomal protein concentration as determinants in the prediction of the fraction unbound in microsomal incubations. Drug Metab. Dispos. 2008, 36, 535−542.

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