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Preclinical Dose Number and its Application in Understanding Drug Absorption Risk and Formulation Design for Preclinical Species W Peter Wuelfing, Pierre Daublain, Filippos Kesisoglou, Allen Templeton, and Caroline McGregor Mol. Pharmaceutics, Just Accepted Manuscript • DOI: 10.1021/mp500504q • Publication Date (Web): 11 Feb 2015 Downloaded from http://pubs.acs.org on February 18, 2015

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Molecular Pharmaceutics

Preclinical Dose Number and its Application in Understanding Drug Absorption Risk and Formulation Design for Preclinical Species W. Peter Wuelfing1,5,*, Pierre Daublain2,5,*, Filippos Kesisoglou3, Allen Templeton1, Caroline McGregor2 1 Analytical

Sciences, Merck Research Laboratories, Merck & Co., West Point,

PA 19486 2 Discovery

Pharmaceutical Sciences, Merck Research Laboratories, Merck & Co., Boston, MA 02115 3 Biopharmaceutics, Merck Research Laboratories, Merck & Co., West Point, PA 19486 4 Discovery Pharmaceutical Sciences, Merck Research Laboratories, Merck & Co., Kenilworth, NJ 07033 5 To whom correspondence should be addressed (e-mail: [email protected] and [email protected]) Keywords: Keywords Preclinical, dose number, preclinical dose number, FaSSIF, drug absorption, solubility, formulation, drug discovery, dose proportionality ABSTRACT: ABSTRACT: In the drug discovery setting the ability to rapidly identify drug absorption risk in preclinical species at high doses from easily measured physical properties is desired. This is due to the large number of molecules being evaluated and their high attrition rate, which make resource-intensive in vitro and in silico evaluation unattractive. High dose in vivo data from rat, dog, and monkey are analyzed here, using a preclinical dose number (PDo) concept based on the dose number described by Amidon and other authors. PDo as described in this paper is simply calculated as dose (mg/kg) divided by compound solubility in FaSSIF (mg/mL) and approximates the volume of biorelevant media per kg of animal that would be needed to fully dissolve the dose. High PDo values were found to be predictive of difficulty in achieving drug exposure (AUC)-dose proportionality in in vivo studies as could be expected, however, this work analyzes a large dataset (> 900 data points) and provides quantitative guidance to identify drug absorption risk in preclinical species based on a single solubility measurement commonly carried out in drug discovery. PDo values are defined above which > 50% of all in vivo studies exhibited poor AUC-dose proportionality in rat, dog, and monkey and these values can be utilized as general guidelines in discovery and early development to rapidly assess risk of solubility-limited absorption for a given compound. A preclinical dose number generated by biorelevant dilutions of formulated compounds (formulated PDo) was also evaluated and defines solubility targets predictive of suitable AUC-dose proportionality in formulation development efforts. Application of these guidelines can serve to efficiently identify compounds in discovery likely to present extreme challenges with respect to solubility-limited absorption in preclinical species 1 ACS Paragon Plus Environment


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as well as reduce the testing of poor formulations in vivo, which is a key ethical and resource matter. *These authors contributed equally to this work INTRODUCTION The developability of drug candidates has been studied extensively for over a decade.1-9 One of the critical development steps for a compound is to enable

drug

absorption

at

high

doses

(>>

10

mg/kg)

to

allow

pharmacodynamic (PD) and toxicology studies in preclinical species. Assessments to determine suitable formulations for these compounds typically rely on biorelevant dilution or equilibrium solubility measurements which are then used in drug absorption software to predict either AUC (area under the curve for in vivo drug concentration vs. time) or other pharmacokinetic (PK) parameters (such as Tmax).10 Literature examples for these activities are very detailed and interrogate a single or few compounds extensively and such approaches are preferred when resources and time are available.

However, when working in drug discovery, a way to make

predictions on multiple compounds across different chemical structural series is critical to move programs forward efficiently, particularly in light of extremely high attrition rates. Therefore an assessment based on readily available solubility measurements and application of the simplest effective absorption model possible is of necessity.

Led by initial drug absorption

literature by Hilgiers,7 Wuelfing et al. had previously published on the identification of suitable formulations in preclinical species for high dose studies.11 In this previous work they applied a) the Maximum Absorbable Dose (MAD) model as well as b) empirical data analysis of compound solubility in neat formulation versus in vivo results (AUC) to generate general solubility guidelines for selection of preclinical formulations likely to drive drug absorption at high doses. The MAD approach required biorelevant dilutions from formulations and case studies were presented for formulations with surfactants, co-solvents and pH-adjusted in-situ salts. MAD-based in 2 ACS Paragon Plus Environment

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vitro studies, while labor intensive, were shown to predict in vivo results well and therefore allow elimination of poor formulations from consideration. The empirical results were also quite effective at predicting in vivo performance and gave very similar conclusions for identifying reasonable formulations (i.e. those providing adequate solubility for AUC-dose proportionality).

The

dataset was limited to rats and studies were mostly conducted at doses of 100 mg/kg and lower. A significant question remaining after this initial research was whether simple solubility measurements, without significant biorelevant dilution or drug absorption modeling (either MAD or other in silico tools), could be used to assess potential for suitable drug absorption across multiple species, chemical series and at doses higher than 100 mg/kg. The goal of the research presented here is to test this question via preclinical drug absorption predictions with a much larger dataset using dose number considerations solely based on solubility. The dose number concept was initially developed by Oh, Curl, and Amidon in 1993 with multiple papers and book chapters following,12-16 providing a framework for understanding and categorizing drug absorption risk and limitations. The dose number as presented by Amidon reflects the number of human intestinal volumes necessary to dissolve an entire dose, thereby normalizing solubility and dose. This simple construct is powerful as high dose number values reflect scenarios where full solubilization of a given dose will be impossible without extensive drug absorption and further dissolution processes, therefore representing absorption risk. It was logical that this construct would also be useful in predictions for preclinical studies. The application is further laid out in a publication by Rohrs where he generates theoretical fraction absorbed (Fabs) contour plots for preclinical species using physiological scaling factors for dose and dissolution numbers.15 The large in vivo and in vitro dataset presented herein represents a Merck database of discovery and development compounds and covers over 900 AUCdose proportionality values from 92 compounds and 58 chemical series across 3 ACS Paragon Plus Environment


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three preclinical species.

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We utilize a preclinical dose number (PDo)

calculated from equilibrium human FaSSIF solubility as it was the only consistently available solubility value for compounds in the in vivo dataset maximizing the size of the evaluation. The implications of not using speciesspecific intestinal media are discussed later in the paper. The model does not attempt to correct for exact intestinal volumes but rather normalizes dose and body mass across rat, dog, and monkey by dividing dose (mg/kg) by solubility (mg/mL) and PDo carries units of mL/kg.

The preclinical dose

number is therefore not directly relatable to the unitless Do as described for humans in the referenced literature [dose / (volume of intestinal fluid x solubility)] and PDo values are nominally larger than Do values.

Our

analysis evaluates the correlation between in vivo AUC-dose proportionality (DP), a descriptor of bioperformance, and PDo with results showing a clear ability to identify quantitative limits in all three species where drug absorption risk is high simply based on the compound’s equilibrium FaSSIF solubility value and a selected dose. To the authors knowledge this is the largest preclinical in vivo dataset analyzed in such a way as previous papers referenced herein reported detailed evaluation of only a few compounds and doses. The assessment is then expanded with a smaller dataset for which biorelevant dilution of formulations is carried out in vitro to reflect compound gastrointestinal (GI) solubility during dosing, enabling calculation of a formulated PDo. While not as readily generated in the discovery space, the formulated PDo is applied to highlight formulation solubility targets associated with suitable DP which are then compared to PDo and previously published results.11

With consistent application of the PDo model,

compounds with significant absorption risk can readily be identified and formulations

with

low

probability

of

enabling

proportionality can be eliminated from consideration.

EXPERIMENTAL 4 ACS Paragon Plus Environment

suitable

AUC-dose

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Solubility Measurements. Measurements. Compounds were received from Merck Medicinal Chemistry or Process Chemistry and analyzed by the Merck Discovery Pharmaceutical Sciences group.

Human fasted-state simulated intestinal

fluid (FaSSIF) has been utilized per Dressman's work.17 A typical solubility measurement is conducted on approximately 5-10 mg of compound and 0.2-1 mL of FaSSIF (pH 6.5) after stirring for 24 hours at room temperature. The sample is clarified by either filtration with a 0.45 µm centrifuge filter (PVDF) or simple centrifugation at 14,000 rpm for 10-15 minutes. The sample is diluted with diluent, typically ACN:water (1:1, v:v), for analysis by reversephase HPLC with UV-Vis detection. The measurement solids are collected for X-ray diffraction analysis and polarized light microscopy to confirm the final state of the drug during the measurement.

The vast majority of

compounds in our dataset were crystalline in nature at the time of FaSSIF measurement and reflect equilibrium solubility values. When formulated Do is discussed formulations were diluted using guidance from reference 11. A typical dilution includes an initial gastric simulation where formulation is diluted 1:1 (v:v) with human simulated gastric fluid (water acidified to pH 1.8 with 0.01N HCl) followed by a 3:1 (v:v) dilution with human FaSSIF. The solubility is measured at 1 hour using the sample clarification and analysis methods described above.

Formulation Preparation and Types. Types. Compounds were received from Merck Medicinal Chemistry or Process Research. For each in vivo study compounds were formulated by the Merck Discovery Pharmaceutical Sciences group and delivered for in vivo dosing.

Small molecule compounds were typically

formulated by addition of compound and formulation vehicle into a glass vessel with overnight stirring to ensure compound equilibrium solubility and dosed by gavage tube or syringe. The most common formulation vehicles used in the in vivo studies included low levels of aqueous polysorbate 80 (Tween 80), sodium dodecyl sulfate, HCl and NaOH (for in-situ salt 5 ACS Paragon Plus Environment


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formation), and neat PEG 400 (polyethylene glycol 400). All were purchased from and used as received from vendors. Methylcellulose A4C grade was purchased from Dow chemical and used as an aqueous suspending agent. Water used for formulations was filtered with a Millipore system.

Pharmacokinetic Data. All studies were conducted under a protocol approved by a Merck Institutional Animal Care and Use Committee.

Compound

plasma levels over 24 hours were determined by HPLC-MS by the Merck Pharmacokinetics, Pharmacodynamics and Drug Metabolism group. Oral formulations dosed are as described within the paper. Studies were carried out in male Wistar Han rat, Beagle dog, and Rhesus monkey with N = 3 for a large majority of studies. The AUC results reported are the arithmetic mean AUC for all animals within a dose group.

Calculation of preclinical dose number (PDo (PDo). PDo). The preclinical dose number (mL/kg, further used without units throughout this paper) was calculated as in equation 1 where mg/kg refers to the mg of compound per kg of preclinical species body weight.

Preclinical Dose Number PDo =

⁄ !"#$ %"&''() '# *%%+* ⁄,

(1)

Calculation of AUCAUC-Dose proportionality proportionality (DP). Dose proportionality of oral dosing studies was calculated by normalizing the AUC ratio between two doses with the ratio of the tested doses as described in equation 2.

The

benchmark dose was typically between 2 and 10 mg/kg and the selected dose assessed for AUC-dose proportionality ranged between 5 and 1,000 mg/kg in the dataset.

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Dose proportionality DP =

01234546748 9:;4 > 10 mg/kg) to better understand extent of absorption or clearance saturation were not explored. All data in Figure 1a are color- and shape-coded to reflect the range of the doses being evaluated and are binned as < 10 mg/kg, between 10 and 100 mg/kg, or greater than 100 mg/kg. Figures 1b and 1c represent the same plots for dog and monkey studies and contain 299 and 161 in vivo data points, respectively. In Figure 1a and 1b as PDo increases above ~ 5,000 - 10,000 mL/kg the reduced frequency of DP > 1 is visually evident reflecting solubility-limited drug absorption in the two species. In general, higher doses represent larger PDo values in each plot. This is an expected trend given the difficulties in achieving a low PDo at high dose due to typical compound solubility (for example, a 750 mg/kg dose would require an uncommonly large 1 mg/mL FaSSIF solubility value to generate a PDo of 750).

Intermediate doses

between 10 and 100 mg/kg span most of the PDo range in the datasets. Figure 1c, representing in vivo DP results in monkey, also shows similar trends although the data density is lower which makes the correlation less clear. An intuitive method to identify solubility targets generally predictive of successful drug absorption is to define PDo ranges where DP results were “suitable” based on some predetermined definition.

From a practical

standpoint DP values larger than 0.6 commonly enable non-overlapping exposures between two doses based on typical PK variability and are therefore suitable to support discovery and development programs based on the author’s experience (> 100 studies). Values lower than 0.4 are associated with a reduced ability to achieve non-overlapping exposures and are suboptimal. The distribution of in vivo DP for selected PDo ranges (or bins) was 8 ACS Paragon Plus Environment

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evaluated and the percentage of suitable results within those ranges was determined. In Figure 2 DP values were classified in 3 categories: DP > 0.6 (green, suitable in vivo performance), DP = 0.4 - 0.6 (yellow, marginal in vivo performance), and DP < 0.4 (red, poor in vivo performance).

In rats the

percentage of studies with DP > 0.6 decreases as PDo increases, from greater than 80% for PDo < 500 to approximately 40% at PDo = 10,000-50,000 and to less than 20% for PDo > 100,000.

For dogs the trend is also clear with

occurrence of suitable in vivo performance gradually decreasing from greater than 90% at small PDo (< 10) values all the way to 0% at PDo > 100,000. Finally, monkey data also show a strong trend when analyzed this way, with > 80% suitable in vivo results at low PDo (< 500) decreasing to approximately 20% for PDo > 50,000. PDo of 10,000 and 5,000 represent limit values below which DP is > 0.6 for a majority (>50%) of compounds tested in rat and dog, respectively. PDo values below 5,000 also correspond to ~ 50% DP > 0.6 results in monkey as well. In short these proposed PDo target values can be seen as cut-offs above which it is likely to have solubility-limited drug absorption. Such categorical approach limits the contribution of DP data points where definite clearance saturation is occurring, specifically DP > 1. Several statistical approaches were used to further test these proposed PDo target values for rat, dog, and monkey. The first statistical evaluation was conducted using a Pearson's chisquared test for multiple PDo targets ranging from 10 to 1,000,000. Such analysis serves to compare categorical data and evaluate dependence of variables. All PDo values in the dataset were categorized as either “Low PDo” (PDo values below proposed target) or “High PDo” (PDo values above proposed target), and DP values were categorized as “Low DP”, “Moderate DP” and “High DP” for DP < 0.4, DP = 0.4-0.6 and DP > 0.6, respectively. Contingency tables were then created to represent the frequency distribution of data points based on DP and PDo categories (as illustrated in Table S1 for a PDo cut-off of 10,000 in rat), and the chi-squared test statistic χ2 was 9 ACS Paragon Plus Environment


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calculated. The χ2 values obtained for most of the proposed PDo targets were well above 9.21, the upper-tail critical value from the chi-squared distribution for a confidence of 99% (with 2 degrees of freedom), which confirms the statistical dependence between DP and PDo.

Furthermore PDo limits

associated with the largest χ2 values were 10,000, 5,000 and 5,000 for rat, dog, and monkey, respectively (Tables S2-S4), and hence match the previously estimated targets.

The agreement is satisfying and helps to

confirm proposed these targets as relevant in defining general regimes with potential for solubility-limited absorption. ANOVA also confirmed that the proposed PDo targets are statistically meaningful. For each species DP values were separated into two groups for the analysis. The first group consisted of DP data for PDo values smaller than the proposed PDo targets (i.e. all DP data for PDo values < 10,000 for rat). The second group consisted of DP data for PDo values larger than the defined proposed PDo targets (i.e. all DP data for PDo values > 10,000 for rat). For all species ANOVA yielded p-values < 0.001 for differences in DP values between the two groups, which is significant at the 95% confidence level (Table S5).18 The geometric mean DP values for PDo below targets (i.e. favorable solubility with respect to dose) were 1.0, 0.8, and 1.5, for rat, dog, and monkey, respectively; while the mean DP values for PDo above PDo targets (i.e. less favorable solubility with respect to dose) were 0.4, 0.3, and 0.5. Thirdly, plots of median DP and median absolute deviation versus selected ranges of PDo values for each species show clear trending to lower DP at higher PDo (Figure S1). Results again show similar PDo values at which median DP falls below 0.6 for rat, dog and monkey (~ 10,000, 5,000 and 5,000, respectively). For monkey median results in the 10,000-50,000 range are elevated by the presence of many DP >> 1 at PDo values just larger than 10,000 (see Figure 1c).


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Finally, regressions yielded poor fit to the data and were therefore not pursued over the statistical approaches above. Clearly drug absorption risk is a gradient elevating at higher PDo values, however the proposed PDo targets (10,000 and 5,000 for rat and monkey/dog, respectively) can be used as general limits above which absorption risk becomes significant due to limited compound solubility. Application of these target PDo values allows the discovery or development scientist to rapidly evaluate whether the inherent solubility of a discovery compound poses a threat to drug absorption in preclinical species via the use of a single, simple FaSSIF solubility measurement. As described earlier, of note in the dataset is the existence of significant DP values well above 1 indicating saturation of clearance mechanisms. In rat, dog, and monkey, values greater than 1 account for > 25% of the data.

This is less frequently seen at high doses due to the

competing effect of solubility-limited absorption. To better understand this effect, the correlation between dose, PDo and DP was evaluated as illustrated in Figures 3, S2 and S3, for rat, dog and monkey, respectively. PDo values were binned in 4 categories (10,000) and doses were binned in 3 categories (100 mg/kg). Box plots were then used to evaluate how dose impacts DP within each PDo category. The solid boxes reflect DP data points between the 25th and 75th percentiles for each PDo category (y axis in logarithmic scale), while the median DP value is represented by a horizontal white line. Some trends are noted allowing PDo to further be broken into 3 regions with respect to the impact of dose on AUC-dose proportionality. The first region consists of low PDo examples (PDo < 100) where a majority of DP values are above 1 (47 of 76 data points in rat – Figure 3), consistent with solubility not generally limiting drug absorption and some extent of clearance saturation. In that region an increase of median DP with increasing dose is actually observed. This could be attributed to more effective clearance saturation processes at 11 ACS Paragon Plus Environment


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higher doses where compound exposure is greater. The same general trends are seen in dog and monkey (Figures S2 and S3).

The second region is

populated with intermediate PDo examples in the 100 to 10,000 range. The examples in that region display close to dose-proportional exposures up to 100 mg/kg, with common loss in mean dose proportionality at higher doses. The results in that range are likely due to a combination of saturation of clearance mechanisms and solubility-limited absorption.

Finally, a third

region consists of high PDo examples (PDo > 10,000). Most examples in that region display poor AUC-dose proportionality (regardless of the dose) indicating that solubility limitations are severe at high PDo. The dataset does not allow definitive deconvolution of solubility limitation and clearance saturation. However, the analysis of the low PDo studies (< 100) in rat, where no solubility limitations are expected and over 60% of DP values are above 1 serves as a reasonable place to estimate the extent of clearance saturation across the rat dataset. Figure 3 indicates a modest increase in DP with dose for PDo < 100, as indicated by escalation in the median DP value from 1.1 for doses < 10 mg/kg (N = 41 data points), to 1.3 for doses in the 10-100 mg/kg range (N = 27), and to 1.4 for doses above 100 mg/kg (N = 8). If all dosed drug is absorbed based on the favorable solubility scenario presented for PDo < 100, these results suggests that clearance mechanism saturation contributes a 10-30% of AUC across the 10 to 100 mg/kg dose range. While this estimate comes from a small subsection of the larger dataset the median values do not suggest a dominating systematic bias from clearance saturation in our dataset consistent with the ability to find DP – PDo trends. Based on the low N for doses > 100 mg/kg further evaluation should be carried out as more data become available. While PDo enables quantitation of what solubility values reflect drug absorption risk at given doses, the FaSSIF solubility used in analysis does not completely reflect the solubility experienced in the preclinical species GI tract.

First, human FaSSIF was used to measure solubility rather than 12 ACS Paragon Plus Environment

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preclinical species-specific fluids with closer representations of pH and bile salt levels.

Literature suggests that human, dog, and rat bile salt levels

differ in the fasted state and are approximately 3, 5, and 20 mM, respectively.19 No references were found for documented monkey bile salt concentrations. Based on this literature the larger PDo target of 10,000 for rat could be explained by a more solubilizing environment with larger bile salt concentrations present in vivo. Rat and monkey stomach pHs20 in the fasted state appear to be relatively similar in the 3-4 range while dog is slightly higher which could also affect the ionization state of some compounds. Gastrointestinal ionization due to higher pH in animal stomachs certainly could impact absorption for given weak base compounds and would be more pronounced at lower doses where more of the drug could actually be solubilized. In addition, solubility assessment for the compounds presented here was conducted at room temperature and values measured at 37 °C will certainly be larger.21 Measurements rendering higher solubility would yield the same trends with lower PDo targets signaling absorption risk. Finally, the solubility values were not collected from a full dissolution profile. The impact of particle size on dissolution rate and on fraction absorbed is well documented,12,15,16 but for the scope of this paper we have removed it from consideration in our analysis. A key point in the analysis is that the simple equilibrium FaSSIF measurement used to calculate preclinical dose number (PDo) does not reflect any formulation enhancement to preclinical in vivo intestinal solubility. As such the primary hypothesis tested is whether poor equilibrium solubility in FaSSIF is suitable in predicting solubility challenges in vivo, regardless the formulation approach. Thirty five percent of the formulations used in vivo were

carried

out

using

the

simple

suspending

agent

0.5%

(w/v)

methylcellulose and in these cases the in vitro FaSSIF solubility measurement should represent the in vivo state reasonably well.

The

majority of these examples in the dataset are from high solubility compounds 13 ACS Paragon Plus Environment


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in low PDo scenarios. However, in the remaining in vivo studies (ca. 65% of studies) solubilizing formulation approaches were applied. The majority of these formulations were suspensions and solutions in aqueous surfactants aiding overall solubilization in the initial neat formulation as well as during dilution through the GI tract. To a lesser extent solution formulations using co-solvents such as PEG 400 and pH-adjusted in-situ salts were used. In these cases in vitro FaSSIF solubility is likely lower than that of the true in

vivo intestinal state. These solubilizing formulations are represented mostly in the moderate to high PDo studies. The proper lens to view a compound with a PDo < 10,000 or 5,000 for rat and dog/monkey, respectively, is that it is expected to yield suitable DP when properly formulated and dosed in vivo. Disparity in the extent of solubilization provided by formulations as well as potentially different crystalline phases forming from drug precipitating from formulations using PEG 400 or in-situ salts in vivo are likely causes of some of the DP variability seen in the dataset. Regardless of these differences in

vivo the simple FaSSIF evaluation for PDo appears to well predict suitable DP. In order to evaluate usage of the PDo concept to inform on a specific formulation’s ability to deliver suitable DP results we now discuss a smaller

in vitro - in vivo dataset which uses solubility data that more closely reflect behavior of formulated compounds.

Studies Using PDo as a Guide to Identify Suitable Preclinical Formulations The FaSSIF-based PDo has been described above as predictive of suitable in vivo AUC-dose proportionality (DP). It is particularly beneficial based on the ubiquity of FaSSIF solubility measurements making the risk assessment simple and readily achieved. In some instances it may also be desirable to conduct a more detailed “formulated PDo” analysis where solubility of formulated compound diluted in simulated GI fluids is measured so as to better reflect in vivo state and use the results for formulation 14 ACS Paragon Plus Environment

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development. These solubility data are more resource intensive to collect than the simpler equilibrium FaSSIF PDo values, and are often not available in early stages of pharmaceutical profiling during either lead identification or lead optimization. Figure 4 shows the analysis of a smaller dataset from internal programs at Merck using formulated PDo (N=90), with data for all species represented in a single plot. Species were not differentiated due to lower overall data density.

Biorelevant dilution studies were carried out

based on the methods presented in reference 11. In general these can be described as a dilution of formulation with gastric fluid 1:1 (v:v) at room temperature for 1 hour followed by a 4 fold dilution with FaSSIF and final solubility measurement after 1 hour at room temperature. Formulated PDo was then calculated based on this value (dose divided by solubility). Therefore each data point reflects an in vitro measurement of compound solubility that most closely reflects solubilization in the preclinical GI tract. A similar trend is seen as in Figure 1a-1c where more instances of DP < 1 (scenarios suggesting solubility-limited drug absorption) are noted at higher formulated PDo values. Using the same binning analysis as that carried out in Figure 2 absorption risk is still a gradient with elevating formulated PDo values and a formulated PDo target of ~ 500 - 1,000 for suitable DP (> 0.6) can be observed (Figure 5). This ~10-fold shift versus targets for the FaSSIFbased PDo is reasonable and reflects enhanced intestinal solubility provided by effective formulation approaches. DP values were again separated into two groups (DP data associated with PDo greater or lower than the cut-off value of 500) where ANOVA confirmed statistical difference using a 95% confidence interval (p-value < 0.001, with geometric means of 1.3 and 0.3 below and above formulated PDo cut-off, Table S6). Table 1 contains data from a proprietary Merck compound (compound A) formulated and dosed at multiple levels and provides an example of application for the PDo and formulated PDo concepts.

The crystalline

anhydrous free form of the compound is poorly soluble in FaSSIF (0.008 15 ACS Paragon Plus Environment


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mg/mL). The compound is weakly basic (calculated pKa of 1.7 and 12.6) with minimally improved solubility at simulated gastric pH. PDo and formulated PDo as reported in the table are calculated using solubility of the crystalline compound in FaSSIF and solubility after biorelevant dilution (gastric fluid followed by FaSSIF) of compound formulations, respectively.

Dose,

formulation type, and DP are also presented for each study. Based on the poor solubility of the crystalline form, extremely high PDo values of 25,000 and 75,000 are calculated for doses of 200 and 600 mg/kg in rat, respectively. This PDo assessment suggests that the compound has a low probability of being formulated successfully for a DP > 0.6 in rat. In order to address the poor solubility, solubilizing surfactant based formulations were progressed to

in vivo studies.

Biorelevant dilution of the formulations resulted in a

formulated PDo range of approximately 3,000 - 9,000 across the 200 to 600 mg/kg dose range in rat. Given both the initial PDo value for the compound (PDo >> 10,000 at 600 mg/kg) and the subsequent formulated PDo (>> 500 1,000) the plateauing in exposures and low DP value (< 0.3) at high dose despite formulation optimization is not surprising.

Dog studies were

conducted at a lower dose range of 10 - 90 mg/kg, corresponding to a PDo range of 1,200 - 11,250 and a formulated PDo range of 140 - 1,300 for the formulation used. The preclinical dose number suggest that a dose of 90 mg/kg study presents some drug absorption risk which is indeed seen in the drop of DP from 2.0 at 30 mg/kg to 1.1 at 90 mg/kg (this example clearly presents some clearance saturation).

An optimized formulation was later

presented to dogs at 90 and 180 mg/kg with a ca. 2 fold higher expected intestinal solubility as measured at the 1 hour time point post biorelevant dilution.

While the PDo reflected significant risk for poor AUC-dose

proportionality the formulated PDo achieved with the optimized formulation suggested reduced risk (formulated PDo of ~ 530 and 1,100 at 90 and 180 mg/kg, respectively). This was confirmed in the in vivo results where a DP of ~ 3 was noted for both studies. This is a particularly relevant example of 16 ACS Paragon Plus Environment

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both metrics where the PDo reflected high risk while in vitro formulation screening and the resulting formulated PDo indicated the possibility of suitable bioperformance upon significant formulation enhancement. These cases would be expected upon extreme formulation modulation of expected GI solubility, in this case ~ 21X that of the crystalline equilibrium value. If the optimized formulation was used in rat it is still unlikely that a significant difference in DP would be seen (as the formulated PDo for the optimized formulation at a 600 mg/kg dose is ~ 3,500 and thus well above the 500 – 1,000 target). In their previous paper11 Wuelfing et al. suggested that linear drug absorption to 100 mg/kg could be achieved by solubilizing compounds to an in

vivo intestinal solubility of ~ 0.5 mg/mL. This finding was determined by comparing in vitro results from biorelevant dilutions to in vivo study results. This is consistent with the analysis presented here as a compound with in

vitro FaSSIF solubility of 0.5 mg/mL would yield low risk PDo values of 200 and 1,000 for 100 and 500 mg/kg doses, respectively, reflecting that nonsolubilizing formulations approaches, such as simple aqueous suspensions, should enable suitable DP.

A more direct comparison to these previous

results can be made with formulated PDo.

A compound formulated to

achieve 0.5 mg/mL solubility in the intestines suggests an elevated absorption risk (DP) risk at doses above 250 mg/kg as formulated PDo becomes > 500. The PDo evaluation seems to indicate lower overall solubility needs to drive drug absorption than calculated by the maximum absorbable dose model.7,11 To better understand results more research should be aimed at understanding the actual extent of drug absorption and drug clearance saturation mechanisms in these in vivo studies.

Future Directions / Model Improvements In reality a gradual increase in the frequency of poor DP cases with increasing preclinical dose number (PDo) is seen in Figure 2 and 5 and 17 ACS Paragon Plus Environment


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therefore a finer analysis could be envisioned to better assess likelihood of solubility-limited absorption. In addition, while this PDo model has been enabled by using readily-available FaSSIF solubilities it may ultimately be refined using biorelevant media representative of species of interest in order to increase its accuracy. Moreover, particle size effects were not considered in this paper and analyses were carried out as if dissolution kinetics were not rate limiting. This assumption is made as the particle size of early medicinal chemistry batches are not recorded and as such are not available for more detailed analysis. Certainly more soluble compounds will be less impacted than insoluble compounds at a given particle size. Future research should aim to incorporate some measurement of particle size data into in silico predictions on a large dataset such as the one used in our analysis to evaluate whether improved predictions are enabled.

With solubility and

particle size considered, future research should evaluate fraction absorbed (Fabs) for high dose preclinical studies in addition to DP calculations. Finally, only a minor effect of permeability was observed on DP results and conceptually highly permeable compounds should require less solubilization and be less sensitive to any in vivo dissolution limitations.

Conclusions The dose number convention has been extended and shown to be a valuable method to predict solubility-limited absorption in three preclinical species. The preclinical dose number (PDo, in units of mL/kg), defined as the ratio of dose (mg/kg) divided by FaSSIF solubility (mg/mL), was shown to be statistically meaningfully in predicting absorption risk in rat, dog, and monkey studies.

Analysis was carried out on a large number of in vivo

studies to develop simple targets of PDo < 10,000 in rat and PDo < 5,000 in dog

and

monkey

that

were

associated

with

improved

AUC-dose

proportionality (DP). PDo is a simple descriptor since it is based on the most commonly available solubility measurement collected by pharmaceutical 18 ACS Paragon Plus Environment

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profiling groups. It can be utilized to immediately predict drug absorption risk at high doses prior to pharmacodynamic or toxicology studies. The same evaluation carried out using biorelevant dilutions from formulations was further used to identify solubility targets for formulated compound when resources allow for detailed investigation and hence guide formulation design efforts. Based on our limited dataset (N ~ 90) these formulated PDo targets (ca. 500 - 1,000) are approximately 10 fold lower than for compound solubility in FaSSIF alone and reflect effective formulation efforts to improve in vivo solubility. While useful for screening compounds, both PDo and formulated PDo targets should also be utilized by preclinical study formulators to improve the probability of success of AUC-dose proportionality and reduce the burden of animal resources in discovery.

Acknowledgments The authors would like to thank Michael Altman (Structural Chemistry, Merck Research Laboratories, Merck & Co), Craig McKelvey (Research Pharmacy, Merck Research Labs, Merck & Co) as well as Dennis Leung, Robert Saklatvala, Stephanie Gratton-Barrett and John Higgins (Discovery Pharmaceutical Sciences, Merck Research Laboratories, Merck & Co) for their help with data extraction and review of the manuscript. References 1. Palucki, M.; Higgins, J.; Kwong, E.; Templeton, A.C. Strategies at the Interface of Drug Discovery and Development: Early Optimization of the Solid State Phase and Preclinical Toxicology Formulation for Potential Drug Candidates. J. Med. Chem. 2010, 2010 53, 5897- 5905. 2. Thakker, D.R. Strategic Use of Preclinical Pharmacokinetics Studies and In vitro Models in optimizing ADME Properties of Lead Compounds. In Biotechnology: Pharmaceutical Aspects, Volume IV:

Optimizing the "Drug-Like" Properties of Leads in Drug Discovery. Borchardt, R.T.; Hageman, M.J.; Stevens, J. L.; Kerns, E.H.; Thakker, D.R., Eds;. Springer, New York, 2006; 2006 pp 1 – 24. 3. Gan, L-S.; Lee, F.W.; Nelamangala, N.; Li, P.; Labutti, J.; Yin, W.; Xia, C.; Yang, H.; Uttamsingh, V.; Lu, C.; Pulsakar, S.; Daniels, J.S.; Huang, R.; Qian, M.; Wu, J-T.; Cardoza, K.; Balani, S.K.; Miwa, G.T. Case History – Use of ADME Studies for Optimization of Drug Candidates. In Biotechnology: Pharmaceutical Aspects, Volume IV: 19 ACS Paragon Plus Environment


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Optimizing the "Drug-Like" Properties of Leads in Drug Discovery. Borchardt, R.T.; Hageman, M.J.; Stevens, J. L.; Kerns, E.H.; Thakker, D.R., Eds;. Springer, New York, 2006; 2006 pp 81 – 98. 4. Hageman, M.J.; Solubility, Solubilization and Dissolution in Drug Delivery during Lead Optimization. In Biotechnology: Pharmaceutical

Aspects, Volume IV: Optimizing the "Drug-Like" Properties of Leads in Drug Discovery. Borchardt, R.T.; Hageman, M.J.; Stevens, J. L.; Kerns, E.H.; Thakker, D.R., Eds;. Springer, New York, 2006; 2006 pp 99 - 130. 5. Caldwell, G.W. Compound optimization in early- and late-phase drug discovery; acceptable pharmacokinetic properties utilizing combined physicochemical in vitro and in vivo screens. Curr. Opin. Drug Dis. Develop. 2000, 2000 3, 30 – 41. 6. Chen, X-Q.; Antman, M.D.; Christoph, G.; Gudmundson, O.S. Discovery Pharmaceutics – Challenges and Opportunities. The AAPS Journal. 2006, 2006 8, Article 46 E402 – E407. 7. Hilgers, A.R.; Smith, D.P.; Biermacher, J.J.; Day, J.S.; Jensen, J.L.; Sims, S.M.; Adams, W.J.; Fiis, J.M.; Palandra, J.; Hosley, J.D.; Shobe, E.M.; Burton, P.S. Pharm Res. 2003, 20, 1149-1155. 8. Johnson, T.W.; Dress, K.R.; Edwards, M. Using the Golden Triangle to optimize clearance and oral absorption. Bio & Med Chem Lett. 2009, 2009 19, 5560 – 5564. 9. (a) Curatolo, W. Physical Chemical Properties of Oral Drug Candidates in the Discovery and Exploratory Development Settings, Pharm. Sci. Technol. Today, 1998, 1998 (b) Sun, D.; Yu, L. X.; Hussain, M. A.; Wall, D. A.; Smith, R. L.; Amidon, G. L. In vitro testing of drug absorption for drug 'developability' assessment": Forming an interface between in vitro preclinical data and clinical outcomes, Current Opinion in Drug Discovery and Development. 2004, 2004 7, 75 – 85. (c) Gu, C-H.; Li, H.; Levons, J.; Lentz, K.; Ganghi, R. B.; Raghavan, K.; Smith, R. L. Predicting Effect of Food on Extent of Absorption Based on Physicochemical Properties Pharm Res. 24, 2007, 2007 1118 – 1130. 10. (a) Gao, Y.; Carr, R.; Spence, J.K.; Wang, W.W.; Turner, T.M.; Lipari, J.M.; Miller, J.M. A pH-Dilution Method for Estimation of Biorelevant Drug Solubility along the Gastrointestinal Tract: Application to Physiologically Based Pharmacokinetic Modeling. Mol. Pharmaceutics. 2010, 2010 5, 1516 – 1526. (b) Bhattachar, S. N.; Perkins, E.J.; Tan, J.S.; Burns, L. J. Effect of Gastric pH on the Pharmacokinetics of a BCS Class II Compound in Dogs: Utilization of an Artificial Stomach and Duodenum Dissolution Model and GastroPlusTM Simulations to Predict Absorption J. Pharm. Sci. 2011, 11, 4756 – 4765. (c) Kesisoglou, F. Use of Preclinical Dog Studies and Absorption Modeling to Facilitate Late Stage Formulation Bridging for a BCS II Drug Candidate AAPS PharmTech, 2014, 2014 15, 20 – 28. (d) Kuentz, M.; Nick, S.; Parrott, N.; Rothlisberger, D. A strategy for preclinical formulation development 20 ACS Paragon Plus Environment

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using GastroPlusTM as pharmacokinetic simulation tool and a statistical screening design applied to a dog study Eur. J. Pharm. Sci. 2006, 2006 27, 91-99. 11. Wuelfing, W.P; Kwong, E.K; Higgins, J.P. Identification of Suitable Formulations for High Dose Oral Studies in Rats Using In Vitro Solubility Measurements, the Maximum Absorbable Dose Model, and Historical Data Sets Mol. Pharmaceutics. 2012, 9, 1163-1174. 12. Oh, D.M; Curl, R.; Amidon, G. Estimating the Fraction Dose Absorbed from Suspensions of Poorly Soluble Compounds in Humans: A Mathematical Model. Pharm. Res. 1993, 10, 264 – 270. 13. Martinez, M.; Amidon, G. A Mechanistic Approach to Understanding the Factors Affecting Drug Absorption: A Review of Fundamentals J. Clin. Pharmacol. 2002, 2002 42, 620-643. 14. Dahan, A.; Miller, J.; Amidon, G.L. V.P. Prediction of Solubility and Permeability Class Membership: Provisional BCS Classification of the World’s Top Oral Drugs. AAPS Journal 2009, 2009 11, 740 - 746. 15. B. Rohrs. Biopharmaceutics Modeling and the Role of Dose and Formulation on Oral Exposure. In Biotechnology: Pharmaceutical

Aspects, Volume IV: Optimizing the "Drug-Like" Properties of Leads in Drug Discovery. Borchardt, R.T.; Hageman, M.J.; Stevens, J. L.; Kerns, E.H.; Thakker, D.R., Eds;. Springer, New York, 2006; 2006 151 - 166. 16. Sugano, K.; Okazaki, A.; Sugimoto, S. Solubility and Dissolution Profile Assessment in Drug Discovery Drug Metab. Pharmacokinet. 2007, 2007, 22, 225-254. 17. Dressman, J.B.; Amidon, G.L.; Reppas, C.; Shah, V.P. Dissolution Testing as a Prognostic Tool for Oral Drug Absorption: Immediate Release Oral Dosage Forms. J. Pharm Sci. 1998, 1998, 1, 11 – 22. 18. One-way unstacked ANOVA of log normalized DP data from rat, dog, and monkey yielded p-values of < 0.001. See Table S5 for further analysis details. 19. Sugano, K.; Computational Oral Absorption Simulation for LowSolubility Compounds Chem. and Biodiversity 2009, 2009 6, 2014 – 2029. 20. Physiology, and Biochemistry of Humans and Commonly Used Laboratory Animals Biopharm and Drug Disp. 1995, 1995 16, 351 – 380. 21. Mota, F.L.; Carneiro, A.P.; Queimada, A.J.; Pinho, S.P.; Macedo, E.A Temperature and solvent effects in the solubility of some pharmaceutical compounds: Measurements and modeling Eur. J. Pharm Sci. 2009, 37, 499 – 507.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1. AUC-Dose proportionality ratio (DP) as a function of preclinical dose number (PDo) in a) Rat, b) Dog, and c) Monkey Figure 2. 2 Distribution of AUC-dose proportionality ratio (DP) for different preclinical dose number (PDo) categories in rat, dog and monkey Figure 3. 3 Effect of dose on AUC-dose proportionality ratio (DP) within a preclinical dose number (PDo) range in rat Figure 4. 4 AUC-dose proportionality ratio (DP) as a function of formulated preclinical dose number (PDo) in Rat, Dog, and Monkey Figure 5. 5 Distribution of AUC-dose proportionality ratio (DP) for different formulated PDo categories in rat, dog and monkey


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Table 1. 1. Solubility, Preclinical Dose Number, AUC, and Formulation Details for In Vivo Study (Compound A) A) Expected solubility in Compound intestines as equilibrium Dose Formulation determined by Formulated AUC DP Species FaSSIF PDo (mg/kg) Type biorelevant PDo (µM.h) solubility dilutions of (mg/mL) formulations (mg/mL) Rat

10

0.008

1,250

Aqueous surfactant

Not tested

N/A

29

N/A

Rat

200

0.008

25,000

Surfactant suspension

0.07

2,857

170

0.3

Rat

400

0.008

50,000

Aqueous surfactant suspension

0.07

5,714

305

0.3

75,000


0.07

8,571

263

0.2

Rat

600

0.008



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Dog

10

0.008

1,250


Dog

30

0.008

3,750


0.07

429

227

2.0

0.07

1,286

373

1.1

0.17

529

926

2.7

Dog

90

0.008

11,250


Dog

90

0.008

11,250

Optimized† aqueous suspension

0.07

143

38.6

N/A

Optimized Dog 180 0.008 22,500 aqueous 0.17 1,059 2,180 3.1 suspension † Optimized aqueous suspension generating higher solubility values upon biorelevant in vitro dilution


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AUC-Dose proportionality ratio (DP) as a function of preclinical dose number (PDo) in a) Rat, b) Dog, and c) Monkey 345x241mm (96 x 96 DPI)

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346x245mm (96 x 96 DPI)



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349x247mm (96 x 96 DPI)


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Distribution of AUC-dose proportionality ratio (DP) for different preclinical dose number (PDo) categories in rat, dog and monkey 395x160mm (96 x 96 DPI)



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Effect of dose on AUC-dose proportionality ratio (DP) within a preclinical dose number (PDo) range in rat 114x62mm (96 x 96 DPI)


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354x246mm (96 x 96 DPI)



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Distribution of AUC-dose proportionality ratio (DP) for different formulated PDo categories in rat, dog and monkey 344x237mm (96 x 96 DPI)


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Preclinical Dose Number and Its Application in ... - ACS Publications

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