Analyzing the Potential Root Causes of Variability of Pharmacokinetics

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Analyzing the Potential Root Causes of Variability of Pharmacokinetics in Preclinical Species Pierre Daublain, Kung-I Feng, Michael D Altman, Iain Martin, Suman Mukherjee, Rebecca Nofsinger, Alan B. Northrup, Richard Tschirret-Guth, Mark Cartwright, and Caroline McGregor Mol. Pharmaceutics, Just Accepted Manuscript • Publication Date (Web): 22 Mar 2017 Downloaded from http://pubs.acs.org on March 22, 2017

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Molecular Pharmaceutics

Analyzing the Potential Root Causes of Variability of Pharmacokinetics Pharmacokinetics in Preclinical Species Pierre Daublain1,11*, Kung-I Feng2,11*, Michael D. Altman3, Iain Martin4 Suman Mukherjee5, Rebecca Nofsinger6, Alan B. Northrup7, Richard Tschirret-Guth8, Mark Cartwright9, and Caroline McGregor10* 1 Discovery

Pharmaceutical Sciences, MRL, Merck & Co., Inc., Boston, MA 02115 USA 2 Discovery Pharmaceutical Sciences, MRL, Merck & Co., Inc., Rahway, NJ 07065 USA 3 Chemistry Modeling and Informatics, MRL, Merck & Co., Inc., Boston, MA 02115 USA 4 Pharmacokinetics, Pharmacodynamics and Drug Metabolism, MRL, Merck & Co., Inc., Boston, MA 02115 USA 5 Biochemical Toxicology and Toxicokinetics, MRL, Merck & Co., Inc., West Point, PA 19486 USA 6 Biopharmaceutics & Specialty Dosage Forms, MRL, Merck & Co., Inc., West Point, PA 19486 USA 7 Medicinal Chemistry, MRL, Merck & Co., Inc., Boston, MA 02115 USA 8 Pharmacokinetics, Pharmacodynamics and Drug Metabolism, MRL, Merck & Co., Inc., Kenilworth, NJ 07033 USA 9 Drug Safety, MRL, Merck & Co., Inc., Kenilworth, NJ 07033 USA 10 Analytical Chemistry, MRL, Merck & Co., Inc., Rahway, NJ 07065 USA 11 To whom correspondence should be addressed (e-mail: [email protected] and [email protected]) *These authors contributed equally to this work Keywords: Preclinical, variability, exposure, AUC, FaSSIF, SGF, solubility, permeability, lipophilicity

pharmacokinetics,

ABSTRACT The purpose of this research was to assess variability in pharmacokinetic profiles (PK variability) in preclinical species and identify the risk factors associated with the properties of a drug molecule that contribute to the variability. Exposure data in mouse, rat, dog and monkey for a total of 16,592 research compounds studied between 1999 and 2013 were included in the analysis. Both in vivo study parameters and in silico/experimental physicochemical properties of the molecules were analyzed. Areas under the plasma concentration vs. time curves (AUC) were used to assess PK variability. PK variability was calculated as the ratio of the highest AUC within a defined set of AUC values (AUCmax) over the lowest

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AUC within that set (AUCmin). Both intra- and inter-animal variability were analyzed, with intra-animal exposures found to be more variable than interanimal exposures. While several routes of administration were initially studied, the analysis was focused on the oral route which corresponds to the large majority of datapoints and displays higher variability than the subcutaneous, intraperitoneal or intravenous routes. The association between inter-animal PK variability and physical properties was studied and low solubility, high administered dose, high preclinical dose number (PDo) and pH-dependent solubility were found to be associated with high variability in exposures. Permeability - as assessed by the measured permeability coefficient in the LLC-PK1 cell line - was also considered but appeared to only have a weak association with variability. Consistent with these findings BCS class I and III compounds were found to be less prone to PK variability than BCS class II and IV compounds. A modest association of PK variability with clearance was observed while the association with bioavailability, a higher PK variability for compounds with lower bioavailability, appeared to be more pronounced. Finally, two case studies that highlight PK variability issues are described, and successful mitigation strategies are presented. INTRODUCTION INTRODUCTION

Developability assessment of drug candidates and their progression in preclinical and clinical studies rely on their thorough evaluation in vivo including establishing relationships between dose, exposure and response.1

Efficacy studies in preclinical species are important components of the drug discovery process. While exposure-response representations are more appropriate than dose-response plots to rationalize efficacy results, in practice in vivo readouts such as tumor growth inhibition (oncology studies) are often averaged across animals prior to representation of averaged efficacy results for each dose level. High variability in exposure within dose groups may

impact

the

dose-response

relationship,

thereby

complicating

interpretation of results and affecting usefulness of such experiments.2, 3 PK collection in early studies may be inadequate (collection on specific days only, limited number of animals, use of separate animals for PK, etc.) to enable rationalization of individual efficacy levels when PK variability is high.


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Conducting safety studies in preclinical species also represents a key part of the evaluation of lead molecules as these progress throughout late discovery and early development. Characterization of drug candidates commonly includes an assessment of preclinical safety margins in a preclinical species. Because averaged exposures as well as toxicological findings are assessed at each dose level, variability in pharmacokinetics (PK) may indirectly impact safety margins, which in some cases may represent a major issue for the development of a compound.4 For example, a single animal displaying much higher area under the curve (AUC) than the other animals in its dose group may experience adverse effects and hence cause the dose level to be considered non-tolerated (e.g. medium dose in the theoretical example provided in Figure 1).

Figure 1. Theoretical example of exposure vs. dose representation for a compound administered at three dose levels in a safety study

Finally, in addition to the effects described above, PK variability in humans can have major consequences on the fate of drug candidates in development.5, 6.

It may lead to difficulty in dose finding7 (challenges in the identification of

an efficacious and tolerated dose due to variability), alterations in clinical



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study design – including dosing adjustment, stricter inclusion and/or exclusion criteria, requirement for dosing with food, need for complex formulations and usage of alternate routes of administration –, and may ultimately impact final drug labeling. Importantly, clinical PK variability can lead to termination of drug candidates with low therapeutic index (ratio between the maximum tolerated AUC over the minimum efficacious AUC, see Figure 1).

As described above, the effects of PK variability on drug discovery and development programs can be significant and lead to elongated timelines, increased resource requirement and/or program termination. PK variability in humans has been extensively studied.8-14 Nicolas et al. described how gender differences, genetic polymorphism, and food intake can act as major factors for variability in pharmacokinetics.10 A question-based approach to evaluate

the

potential

impact

of

pharmacogenetics

on

clinical

pharmacokinetic variability was described by Shaw et al.11 Modeling approaches

including

physiological,

biochemical

and

physicochemical

parameters have also been reported,12 and in particular the impact of CYP3A4 oxidation13 and glucuronidation by UGT14 was studied. However, to our knowledge, no detailed analysis of variability in preclinical species has been reported. In addition, the association of physicochemical properties with human PK variability has not been extensively studied. The hypothesis for the study presented here was that early indicators, including physical attributes, could guide discovery teams to identify and further derisk variable compounds prior to committing to preclinical safety studies or additional structure-property relationship (SPR) efforts. The objective was to identify study parameters and compound properties that may inform on PK variability risk in preclinical species, as well as to develop mitigation strategies to derisk compounds demonstrating high risk factors.


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Based on reported analyses of preclinical and clinical PK variability,15-19 the following three types of variability were considered in the manuscript: •

Inter-animal variability16, 19: variability in exposure between different animals in the same dose group

•

Intra-animal variability (or inter-occasion variability)16,

17:

variability

in exposure from the same animal from independent measurements (same dose level, different dosing events) •

Inter-study variability18: variability in exposure between different studies of similar design (same dose and formulation, etc.)

The limited number of PK datapoints available for inter-study variability assessment did not allow for a robust analysis of such variability. Therefore intra-animal and inter-animal variability were first compared. Our study then focused on inter-animal variability over intra-animal variability due to a much high number of available datapoints (e.g. 30165 vs. 1326 for oral dosing). The authors analyzed the extent of variability in exposures and how it differed between routes of administration, preclinical species (mouse, rat, dog and monkey) and dose. The impact of a number of physicochemical and ADME attributes on PK variability was also investigated. Finally, two case studies are presented and approaches to derisk and mitigate PK variability are provided.

EXPERIMENTAL Chemicals and reagents. Multiple formulations were utilized for the reported

in vivo studies. The most common chemicals and reagents included polysorbate 80 (Tween 80), PEG 300 (polyethylene glycol 300), PEG 400 (polyethylene glycol 400), HCl, NaOH, sodium taurocholate, NaCl, HPLCgrade acetonitrile, and HPLC-grade acetone. Most were purchased from Fisher Scientific and used as received. Dimethyl sulfoxide (DMSO) was



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acquired from ACROS. HPMCAS (hydroxypropylmethylcellulose acetate succinate) LF or MF grade and HPMCP (hydroxypropylmethylcellulose phthalate) were purchased from Shin-Etsu. Methylcellulose A4C grade was purchased from Dow chemical. Hydroxypropyl-β-cyclodextrin was purchased from CTD, Inc. Water used for formulations was filtered using a Millipore system.

Preparation of amorphous solid dispersion formulations formulations used in case studies. studies A mixture of a compound of interest and selected polymer in acetone was spray-dried by a ProCepT Micro-Spray Dryer using a bifluid nozzle with an aperture of 0.6 mm. An inlet temperature of 80 °C was used. The atomization airflow rate was set to 6 L/min and the spray rate to 6 mL/min.

Pharmacokinetic data. All studies were conducted under a protocol approved by our Institutional Animal Care and Use Committee. Compound plasma levels

over

24

Pharmacokinetics,

hours

were

determined

Pharmacodynamics

and

by Drug

LC/MS-MS Metabolism

by

our

group.

Bioavailability (%F) values were calculated from oral and intravenous PK experiments conducted at low doses (typically in the 0.25-2 mg/kg range) as the ratios of dose-normalized AUC for oral dosing over dose-normalized AUC for intravenous dosing. Reported clearance values were calculated as dose over AUC ratios following intravenous administration at low dose.

Assessment of PK variability. Variability in AUC was used as the sole descriptor of PK variability and variability in Cmax or Ctrough was not analyzed. AUC is used as primary indicator of exposures in a majority of our efficacy and safety studies, although Cmax is commonly used for safety margin assessment in cardiovascular safety studies.


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PK variability was calculated as AUCmax/AUCmin, the ratio of the highest AUC within a defined set of AUC values over the lowest AUC within that set. Sets of AUC values were defined as indicated below: •

For inter-animal variability, the dataset consists of AUC values for all animals in the same dosing group and dosing event, i.e. administered on

a

given

day

using

a

defined

compound,

dose,

route

of

administration, formulation, dosing regimen and feeding state (e.g. datapoints represented in Figure 2a) •

For intra-animal variability, the dataset consists of all AUC values for a single animal administered over multiple days using a defined compound, dose, route of administration, formulation, dosing regimen and feeding state (e.g. datapoints represented in Figure 2b)

Figure 2. Theoretical representation of AUC datapoints used in PK variability calculation (AUCmax/AUCmin). a) InterInter-animal variability for a single day study in 5 animals, b) intraintra-animal variability for a single animal study conducted over 5 days

To enable a more robust assessment of variability, only datasets that contained at least three AUC values were considered. Other descriptors of PK variability such as relative standard deviation of AUCs were tested and yielded identical results (data not shown). For most analyses described in the manuscript, the distributions of PK variability categories for different values of a parameter of interest were



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represented. In such analyses four PK variability categories (i.e. AUCmax/ AUCmin categories) of a) equal to or lower than 2 (‘≤ 2’), b) larger than 2 and equal to or lower than 3 (‘2-3’), c) larger than 3 and equal to or lower than 5 (‘3-5’) and d) larger than 5 (‘> 5’) were commonly used. The purpose of research described in this manuscript was to identify study parameters and compound properties associated with PK variability in preclinical species, and - to the authors’ knowledge - to provide a first qualitative analysis of the relation between these factors and PK variability using a large compound dataset. A detailed statistical evaluation of the correlation between PK variability and each parameter/attribute was considered beyond the scope of our study and was not conducted for this first analysis. Future work could involve usage of statistical descriptors (Pearson’s chi-squared tests, ANOVA, etc.) on a more limited number of factors.

High

throughput

log

D7

measurements.

High

throughput

log

D

measurements at pH 7 were conducted from 10 mM DMSO stock solutions in a well plate format using a reverse-phase HPLC retention time method with UV-Vis detection.

High throughput solubility measurements at pH 2 and pH 7. 7. High throughput solubility measurements were conducted in a well plate format by addition of 10 mM DMSO stock solutions to 10 mM pH 2 and pH 7 potassium phosphate buffers (resulting total concentration of 200 µM) followed by 24 h equilibration, filtration and reverse-phase HPLC analysis using UV-Vis detection.

Human FaSSIF and SGF manual solubility solubility measurements. Fasted-state simulated intestinal fluid (FaSSIF) and fed-state simulated intestinal fluid (FeSSIF) have been utilized per Dressman's work.20 Deionized water acidified to pH 1.8 with 0.01 N HCl was used as human simulated gastric fluid (SGF).


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A typical solubility measurement was 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 was clarified by either filtration with a 0.45 µm centrifuge polyvinylidene fluoride filter or simple centrifugation at 14,000 rpm for 10–15 minutes (except for cases of low solubility, assessment of filter binding was not usually conducted). The sample was diluted with diluent, typically acetonitrile:water (1:1, v:v), for analysis by reverse-phase HPLC with UV-Vis detection. The measurement solids were collected for X-ray powder diffraction analysis (XRPD) and polarized light microscopy to confirm the final state of the drug during the measurement. The majority of compounds in our dataset were crystalline in nature at the time of FaSSIF measurement.

Calculation of preclinical dose number. number. As described in a recent publication21, the preclinical dose number (PDo) - used as a dimensionless number - was calculated from the oral dose in mg/kg (where mg/kg refers to the mg of compound per kg of preclinical species body weight) and the compound solubility in FaSSIF in mg/mL using the equation below: Preclinical Dose Number PDo =

Oral Dose mg⁄kg Compound Solubility in FaSSIF mg⁄mL

High Throughput permeability analysis. Permeability analysis was carried out using LLC-PK1 cell lines by our Pharmacokinetics, Pharmacodynamics and Drug Metabolism group using a protocol similar to that previously published.22 Briefly, LLC-PK1 cells were cultured in 96-well transwell culture plates. A compound of interest was dissolved at 1 µM in Hank’s Balanced Salt Solution (HBSS) containing 10 mM HEPES and the resulting solution was added to either the apical (A) or the basolateral (B) compartment of the culture plate. Buffer was added to the other compartment. At t = 3 h, samples were taken out from both sides for LC-MS/MS analysis. The reported



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apparent permeation (Papp) is the average of the Papp for transport from A to B and that for transport from B to A at t = 3 h.

Software. Software Data analysis and plot creation was carried out using TIBCO Spotfire 7.0.

RESULTS AND DISCUSSION Tolerance for PK variability variability Screening PK studies are routinely conducted at ‘low’ doses (normally lower than 2 mg/kg) to provide initial estimates of pharmacokinetic parameters. As such, some level of PK variability is often acceptable for these early studies. While low variability is typically targeted for subsequent multi-dose PK studies, doses are often widely separated (> 3-fold apart) in order to effectively assess AUC-dose proportionality. Similarly, widely separated doses are commonly selected for PK/PD and efficacy studies to cover at least one order of magnitude in exposures and obtain meaningful dose-response. Doses in toxicological studies are often selected > 3-fold apart. Because of a higher tolerance for PK variability in early experiments and typical dose separation in subsequent studies, in our experience and throughout this manuscript, PK variability values ≤ 2 are considered acceptable, while values in the 2-3 range represent a higher risk for the effectiveness of a given in vivo study. PK variability values greater than 3 often present dose overlapping exposure problems, particularly in drug safety studies.

IntraIntra-animal vs. interinter-animal PK variability Figure 3 provides an overall comparison of inter- and intra-animal PK variability for orally

administered compounds (as reflected by


the

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distribution of AUCmax/AUCmin ratios, see experimental section). Data for mouse, rat, dog and monkey were combined for this general study on intravs. inter-animal PK variability as well as for the analysis on route of administration (next section), while data specific to each species were also used when more detailed associations with dose, physicochemical attributes and ADME properties were investigated (later in the manuscript). High variability cases were found to be slightly more prevalent for intra-animal exposures than for inter-animal exposures. The animals in a given dosing group experience the same formulation and the same conditions (including feeding state), and therefore the main source of inter-animal variability may be associated with physiological differences between these animals. These include differences in gastric and intestinal pH, dimensions and surface areas of intestinal tract, transit time and motility, luminal contents, blood and lymph flow23, as well as activity of metabolic enzymes. Intra-animal variability is assessed for the same animal dosed over multiple days. While consistent experimental parameters throughout a given study are typically targeted, a higher intra-animal variability vs. inter-animal variability may reflect multiple factors for the former including compound accumulation in

vivo, saturation of clearance mechanisms, auto-induction, and changes in age or disease state.

Figure 3. Distribution of a) interinter-animal and b) intraintra-animal PK variability in mouse, rat, dog and monkey combined (all doses). Oral administration only. Number



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of datapoints: 30165 and 1326 for inter-animal variability and intra-animal variability, respectively.

Route of administration and PK variability The distribution of PK variability for the most common routes of administration in our dataset is provided in Figure 4 (all species included). Oral exposures (PO) are observed to be the most variable. They are somewhat more variable than subcutaneous (SC) and intraperitoneal (IP) exposures. As expected intravenous (IV) AUCs are the least variable. The highest prevalence of variability for PO dosing may be expected due to the multiple in

vivo processes governing systemic exposure including compound dissolution and absorption.24,

25

Variability is still important for SC and IP dosing

(impact of dispersibility, local precipitation) but it is markedly lower for IV dosing which represents direct administration to the blood stream. A potential additional explanation for the lower variability for IV dosing could have been the lower doses commonly used for such route of administration (doses mostly < 10 mg/kg), however similar trends are observed within fixed dose ranges (Figure S1). The remainder of the manuscript focuses on oral dosing as it represents the vast majority of the 14977 in vivo studies and is the major route of administration used in our safety and efficacy studies.


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Figure 4. Distribution of PK variability for oral (PO), subcutaneous (SC), intraperitoneal intraperitoneal (IP) and intravenous (IV) administration in mouse, rat, dog and monkey combined (all doses). Number of datapoints: 30165, 564, 477 and 9817 for PO, SC, IP and IV dosing, respectively.

Preclinical species, dose and PK variability The distribution of intra-animal PK variability for mouse, rat, dog and monkey was investigated (Figure S2). The analysis indicates a higher PK variability for larger species, the lowest variability being observed in mouse and the highest in dog. Because the results in Figure S2 do not account for potential bias in doses resulting from differences in the types of in vivo experiments conducted in each animal (e.g. mouse models commonly selected for efficacy studies, dog and monkey predominantly used for toxicology studies), the distribution of PK variability across preclinical species and dose categories was then studied, with results provided in Error! Reference source not found.. found.



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Figure 5. Distribution of interinter-animal PK variability for different preclinical species (mouse, rat, dog, and monkey) and dose categories. Number of datapoints: a) 4539, b) 1566, c) 310, d) 14649, e) 1990, f) 737, g) 2795, h) 1117, i) 479, j) 947, k) 722, l) 348.

Unsurprisingly, an overall increase in PK variability with dose is observed as the interplay between animal physiology and physicochemical properties becomes more likely to impact absorption (e.g. cases of solubility-limited absorption). For each dose category, variability is again higher for larger species with a larger proportion of datapoints in the 2-3, 3-5 and > 5 categories. As highlighted in panel (i) of Error! Reference source not found., found. variability was found to be particularly pronounced in dog above 100 mg/kg, with a large proportion of studies displaying PK variability > 3. This observation may reflect higher potential for absorption limitation in dog and/or a high inter-animal variability in physiological parameters for that species. In particular, major variability in dog gastric pH and gastric


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residence time has been reported26. At high doses variability in these parameters likely translates to variability in absorption and in resulting exposures. In addition, dogs are known to be more susceptible to emesis than the other species studied (mouse, rat and rhesus). Higher incidence of emesis at high dose in dog may also contribute to overall PK variability.

Lipophilicity and PK variability A number of physicochemical attributes known to correlate with absorption and their impact on PK variability were then investigated. The distribution of inter-animal PK variability values for different experimental log D at pH 7 (log D7) were first represented (Figure 6).

Figure 6. Distribution of inter inter--animal PK variability for different HPLC log D7 categories in mouse, rat, dog and monkey combined. Number of datapoints: 99, 857, 4853, 4357, 2680, 671 and 449 for log D7 values in the ≤ 0, 0-1, 1-2, 2-3, 3-4, 4-5 and > 5 categories, respectively.

A modest increase in variability with log D7 was observed, potentially related to the combination of 1) the well-known correlation of lipophilicity with solubility (decrease in aqueous solubility with increasing lipophilicity)27,

28

and 2) potential for absorption limitation for poorly soluble compounds. The



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low variability for compounds with log D7 ≤ 0 provides a hint that permeability limitation may not be a strong contributor. Similar trends were observed in mouse, rat, dog and monkey (Figure S3a). Although the number of datapoints becomes smaller at high doses, the relationship with log D7 did not seem to be majorly impacted for doses above 100 mg/kg (Figure S3b).

Solubility, preclinical dose number and PK variability The distribution of inter-animal PK variability for multiple solubility categories was analyzed, using manual solubility measurements in FaSSIF. As shown in Figure 7 (mouse, rat, dog and monkey combined) as well as Figure S4 (individual plots for the four species), PK variability slightly increases with decreasing solubility, further suggesting that factors prone to impact absorption also play a role in increasing PK variability. A similar analysis was conducted using high-throughput (HT) solubility data in pH 7 buffer (Figure S5) but no clear association with PK variability was observed; potentially because of the limited dynamic range for the solubility assay (0 200 µM). In addition, solubilization by bile salts and phospholipids contained in FaSSIF is not represented in pH 7 solubility values so that the buffer is less relevant for in vivo dissolution.


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Figure 7. Distribution of PK variability for different FaSSIF solubility categories in mouse, rat, dog and monkey combined. Number of datapoints: 1448, 1463, 1189 and 631 for manual FaSSIF solubility values in the ≤ 0.01, 0.01-0.1, 0.1-1 and > 1 mg/mL categories, respectively.

To further investigate the impact of solubility limitations on oral absorption and PK variability, the preclinical dose number (PDo) concept was used (Figure 8). This concept - introduced in a previous publication21 - normalizes dose and solubility, thereby enabling rapid assessment of potential for solubility-limited absorption.



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Figure 8. Distribution of PK variability for different preclinical dose number (PDo) categories in mouse, rat, dog, and monkey combined. Number of datapoints: 586, 869, 1117, 821 and 725 for PDo values in the ≤ 10, 10-100, 100-1000, 1000-10000 and > 10000 categories, respectively.

The use of PDo instead of solubility alone further indicated PK variability to be higher when the potential for solubility-limited absorption is stronger. Similar effects were observed individually in mouse, rat, dog and monkey (Figure S6).

The correlation between PK variability and the pH-dependence of solubility was assessed (Figure 9). For this analysis, the HT pH 2 solubility to HT pH 7 solubility ratio was used since pHs for the two media were considered to be reasonable approximates for minimal and maximal gastrointestinal pHs in the preclinical species of interest (despite higher gastric pH in rat). The analysis was conducted for all doses (Figure 9a), as well as for large doses (≥ 100 mg/kg) only (Figure 9b) when potential for solubility limitation is higher.


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Figure 9. Distribution of PK variability values for different pH 2/pH 7 solubility ratios in mouse, rat, dog, and monkey combined at a) all doses and b) doses ≥ 100 mg/kg only Number of datapoints for pH 2/pH7 solubility ratios in the ≤ 0.1, 0.10.33, 0.33-3, 3-10 and > 10 categories, respectively: a) 1819, 752, 8425, 901 and 1156, b) 503, 131, 1318, 159 and 192.

A bell shaped relationship between PK variability and the solubility ratio was observed, with compounds either possessing much higher solubility at low pH (pH 2 / pH 7 solubility ratio > 10) or at high pH (pH 2 / pH 7 solubility ratio ≤ 0.1) being more prone to PK variability. The effect appears to be slightly more pronounced at high doses. A potential cause for higher PK variability when solubility is pH-dependent is described below: •

Compounds with pH 2 solubility >> pH 7 solubility are more likely to dissolve or remain in solution in the acidic environment of the stomach, and have potential to subsequently precipitate as pH increases in the intestinal tract. Variability in gastric pH may affect the extent of dissolution and/or supersaturation for such compounds as these move into the intestine for absorption. In addition, disparity in intestinal pH may then lead to variability in the kinetics and extent of compound precipitation, and therefore to inter-animal differences in the total amount of material in solution at the site of absorption.

•

Compounds with pH 2 solubility 5X variability for at


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least one dose/dosing event in non-rodent studies. Despite the limited number of compounds included in the analysis, it can be hypothesized that uncontrolled in vivo precipitation of basic salts can cause an increased propensity for variable PK. A higher number of examples would need to be evaluated to truly assess the effect of salt formation. Analysis may also be conducted on chloride salts to assess how salt exchange in the stomach may impact exposures and PK variability.

Permeability and PK variability The bell-shaped correlation of passive permeability with lipophilicity has been described in the literature,31,

32

with maximum absorption around log

D7.4 = 2-3. For hydrophilic compounds, the biological membrane behaves as a barrier while for hydrophobic compounds apparent permeability is affected by membrane retention. The low variability for log D7 ≤ 0 highlighted in Figure 6 suggests that permeability limitation may not be a strong contributor to PK variability. That indeed proved to be the case when PK variability distributions were compared for different permeability ranges (Figure S9). The weak association of permeability and inter-animal PK variability is intriguing. It may reflect a low incidence of cases where permeability is truly limiting absorption (i.e. high doses and Papp values 30 mL/min/kg categories, respectively.

The correlation between PK variability and oral bioavailability (%F) was also examined (Figure 12). In both rat and dog the incidence of PK variability was found to be much lower for orally bioavailable compounds than for molecules with low %F. These results again relate potential for PK variability to suboptimal bioperformance. The trends observed upon investigation of %F and clearance may reflect the interrelationship of the two properties (high clearance likely to cause low %F). The stronger trend with %F than with



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clearance may suggest that the process of absorption is more affected by variability than that of elimination. While the extent of absorption (reflected by the fraction absorbed or Fa) appears to be a major contributor to bioavailability in a large number of cases, the current dataset does not enable deconvolution with the fractions escaping gut and liver metabolism (Fg and Fh) that also determine bioavailability. Future work could involve analyses on how Fa, Fg and Fh individually impact PK variability.

Figure 12. Distribution of PK variability for oral bioavailability (%F) categories in a) rat and b) dog Number of datapoints: a) 589, 405, 541, 1533, 1000 and 1083 for bioavailability values in the ≤ 10, 10-20, 20-30, 30-50, 50-75 and > 75 categories, respectively. b) 164, 397, 427 and 670 for bioavailability values in the ≤ 25, 25-50, 50-75 and > 75 categories, respectively.

Finally, a number of experimental parameters such as animal gender and feeding state were investigated. Gender was found to have no overall impact on variability (Figure S10). No noticeable differences between PK variability in fasted and fed animals were observed (for all species combined as shown in Figure S11 as well as individually in mouse, rat, dog and monkey); however the analysis did not capture studies for which dosing regimen was not controlled. While analysis did not provide statistical evidence of an impact of


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feeding on variability, specific case studies discussed in the following section do demonstrate this impact.

Case study study 1

Problem Statement: Compound A was dosed in dogs at 50 mg/kg as an amorphous free base in 10% (w/w) polysorbate 80 in water using a 5 mL/kg dose volume under fed conditions. Dogs were dosed within 30 minutes of feeding. Adequate exposure was achieved (1040-fold AUC margin vs. human target) but high variability in AUC (5.8 fold) was observed (first row in Table 1).

Table 1. Oral exposure of Compound A dosed dosed orally in fed dog at 50 mg/kg

Formulation

Controlled Feeding

AUC(0-∞) Range (µMh)

Variability

Exposure Multiple to Human Target

Amorphous free base in 10% (w/w) Polysorbate 80 in water

No

36.4- 212

5.8X

340-1960X

Amorphous free base in 10% (w/w) Polysorbate 80 in water

Yes

35.5-52.9

1.5X

330-490X

Amorphous dispersion in 0.5% (w/w) methocel in 10 mM aqueous citrate buffer ( pH 4)

Yes

17.4-20.6

1.2X

160-190X

General Properties of Compound A: The properties of Compound A are summarized in Table 2. The compound has a steep pH-dependent solubility profile with a SGF/FaSSIF solubility ratio of ∼2000, and is a BCS IV compound. The PDo is > 10,000 at 50 mg/kg. These risk factors suggest potential for high PK variability of Compound A.



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Table 2. Properties of Compound A

Property

Value

Molecular weight Physical State Glass transition temperature pKa (measured) log D at pH 7 BCS Class Permeability (LLC-PK1) Clearance in dog Half-life in dog Solubility (amorphous material): 0.1N HCl 0.01N HCl Buffer (pH 4-10) SGF FaSSIF FeSSIF

950 g/mol Amorphous free base 159 °C 5 and 6 (basic groups) 2.9 IV 3.2 × 10-6 cm/s 1.1 mL/min/kg 8.2 h 36.2 mg/mL (at pH 1.7) 4.3 mg/mL (at pH 2.6) < LOQ† (at pH ≥ 4) 7.1 mg/mL (at pH 2.8) 0.0033 mg/mL (at pH 6.5) 0.41 mg/mL (at pH 5.0)

† LOQ = 0.5 µg/mL.

Hypothesis: Hypothesis: Feeding regimen was not well controlled in the initial study: dogs were dosed 30 minutes post feeding regardless of food consumption, and the food was not removed after the dogs were dosed. It is recognized that food affects gastric pH, bile salt secretion, gastric emptying time, and transit time along the GI tract26. Gastric pH one hour post feeding has been reported to be sensitive to food consumption in dogs. Therefore, inter-animal variability in feeding regimen including amount of food consumed and frequency / schedule of eating could have introduced variability to the in vivo dissolution of compounds, particularly for compounds with high FeSSIF/FaSSIF and SGF/FaSSIF solubility ratios.

Mitigation Strategy and Results: The feeding regimen was controlled in a subsequent PK study. Dogs were fed with 300 g of PMI Certified Canine diet #5007 and 1 can of Pedigree Meaty Ground Dinner with Chunky Beef. All


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animals started to follow this feeding regimen 3-4 days before conduction of the study. The same formulation of amorphous free base in 10% polysorbate 80 in water as for the initial experiment was dosed at 50 mg/kg. While a 2.5 fold reduction in the safety margin was observed the three dogs showed much improved PK variability of 1.5 fold (second row in Table 1).

Another approach to manage PK variability for compounds like Compound A with pH 2 solubility >> pH 7 solubility is to minimize solubilization in the stomach using a modified formulation, thereby preventing massive and uncontrolled precipitation in the intestine. This is generally done by incorporating polymers as enteric coating agents that protect drugs from degradation/dissolution by gastric acid. While this formulation approach may be effective in mitigating PK variability, it should be taken with caution as oral exposures may be reduced due to the anticipated lack of dissolution in the stomach and need for full dissolution in the intestine. In this case study, an amorphous dispersion of 20% (w/w) Compound A with 80% HPMC-AS-MF was dosed using the same feeding regimen with a dose volume of 5 mL/kg. HPMC-AS was selected as polymer to minimize the solubilization of Compound A in the stomach and the vehicle of 0.5% (w/w) methocel in 10 mM citrate at pH 4 was chosen to prevent solubilization of both polymer and Compound A in formulation. While the exposure was significantly reduced, the PK profiles between the three dogs were found to be very tight with a PK variability in AUC of only 1.2 x (third row in Table 1).

Case study 2

Problem Statement: Compound B was dosed in fed dogs at 100 and 300 mg/kg as an amorphous dispersion (40% drug load by weight in HPMCP) in 0.5% methocel containing 5 mM HCl. Dose volume was 5 mL/kg. High PK variability was observed, with a second peak in the plasma concentration vs.



time curve for some animals at both doses (see Figure 13 for 100 mg/kg). In addition, no increase in exposure was observed between 100 and 300 mg/kg (Table 3).

Male #1 Male #2 Female #1 Female #2

10

Plasma Concentration of Compound B (µM)

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1

0 0

10

20

30

40

50

Time (h)

Figure 13. Plasma concentration vs. time profile for Compound B dosed as 40% (w/w) solid dispersion in fed dogs at 100 mg/kg with no controlled feeding.

Table 3. Oral exposure of Compound B in fed dogs at 100 and 300 mg/kg oral dosing with with no controlled feeding regimen Dose (mg/kg)

Gender

AUC (µ (µMh)

Cmax (µ (µM)

100

Female Male

96.5 (66.3, 127) 92.2 (23.3, 161)

4.42 (2.01, 6.83) 3.26 (1.62, 4.91)

300

Female Male

73.8 (42.4, 105) 90.1 (81.6, 98.6)

3.12 (3.08, 3.15) 6.30 (6.17, 6.42)

General Properties of Compound B: The properties of Compound B are summarized in Table 4. Compound B has poor solubility in buffer across the physiological pH range (< 0.5 µg/mL in pH range 1.5-8), as well as in biorelevant media (1.2 µg/mL in FaSSIF and < 0.5 µg/mL in SGF). Compound B is a BCS IV compound and its PDo is > 10,000 at doses of 100 and 300 mg/kg indicating a risk of high PK variability.


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Table 4. Properties of Compound B

Property

Value

Molecular weight Physical state Melting point pKa (measured) log D at pH 7 BCS Class Permeability (LLC-PK1) Clearance in dog Half-life in dog Solubility (crystalline material): Buffer (pH 1.5-8) SGF FaSSIF FeSSIF

614 g/mol Crystalline free form 247 °C < 2 (basic groups) 3.8 IV 7.9 × 10-6 cm/s 3.0 mL/min/kg 9.2 h < LOQ† < LOQ† 1.2 µg/mL at pH 6.2 7.5 µg/mL at pH 4.7

† LOQ = 0.5 µg/mL.

Hypothesis: Hypothesis: Firstly, as for case study 1, feeding regimen had not been well controlled in this in vivo study. Solubilization of Compound B may be particularly sensitive to the secretion of bile salt in the intestinal fluid so that variability in food intake could contribute greatly to PK variability. Secondly, the solubility of HPMCP is pH-sensitive. Variability in gastric pH may affect dissolution of the polymer, leading to different degrees of phase separation in the amorphous dispersion.

Mitigation Strategy and Results: Feeding regimen was controlled as described in the previous case study. In addition, surfactant (10% polysorbate 80) was added in the vehicle to improve the solubility of Compound B and to minimize the impact of variability in bile salt secretion on oral absorption. The pH of the dosing vehicle was maintained as pH 4 using a buffer so as to prevent HPMCP from leaching out of the amorphous dispersion in the stomach. An amorphous dispersion with lower drug load was also tested to prolong the supersaturation of Compound B in the lower gastrointestinal



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tract and improve oral exposure while managing PK variability at the same time. The following two formulations were tested in fed dogs at 100 mg/kg with controlled feeding (3 animals per group). The first formulation was also tested under fasted state to evaluate food effect in dogs at the same dose. •

Formulation 1: amorphous dispersion (30% (w/w) drug load in HPMCP) in 0.5% methocel acidified to pH 4 with 10 mM citrate buffer: polysorbate 80 (9:1, v:v).

•

Formulation 2: amorphous dispersion (40% (w/w) drug load in HPMCP) in 0.5% methocel acidified to pH 4 with 10 mM citrate buffer: polysorbate 80 (9:1, v:v).

As indicated in Figure 14, Compound B demonstrated a normal plasma concentration vs. time profile in the three arms of the PK study without the second maximum in plasma concentration previously observed. In addition PK variability in AUC was lower than 20% for these three arms. The significant positive food effect observed supports our hypothesis that feeding regimen may have contributed to PK variability. Usage of buffer could also have helped to mitigate the PK variability although the effect cannot be deconvoluted from the feeding regimen control. The changes in drug load from 20 to 30% did not affect the oral exposure in this case study. Finally, Formulation 1 was also dosed in dogs with controlled feeding at 30, 100, and 300 mg/kg (Figure 15). A linear increase in exposure with dose was established in AUC in this dose range.


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30% (w/w) dispersion; Fed 40% (w/w) dispersion; Fed 30% (w/w) dispersion; Fasted

Plasma Concentration of Compound B (µM)

10

1

0 0

10

20

30

40

50

Time (h)

Figure 14. Total plasma concentration vs. time profile of Compound B in dogs with controlled feeding vs. dogs in the fasted state dosed orally at 100 mg/kg with 5 mL/kg dose volume

800

600

AUC (µM-h)

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400

200

0

0

50

100

150 200 Dose (mg/kg)

250

300

Figure 15. AUC vs. dose curve for Compound B (Formulation 1) dosed at 30, 100 and 300 mg/kg in fed dogs with controlled feeding

CONCLUSIONS Study parameters or compound properties considered to be significant risk factors for PK variability are summarized in Table 5.



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Table 5. Major risk risk factors associated with PK variability Risk Factor

Description

Route of administration

Oral dosing

Dose / solubility / preclinical dose number

pHpH-dependence dependence of solubility / salts

High dose (> 100 mg/kg, particularly in dog) Low FaSSIF solubility (≤ 0.01 mg/mL) High PDo (> 10,000) pH-dependent solubility (pH 2 solubility/pH 7 solubility > 10 or ≤ 0.1) Usage of basic salts

BCS Class

BCS Class II or IV

Bioavailability (%F)

Low oral bioavailability (%F ≤ 10% in rat, %F ≤ 25% in dog)

Feeding regimen

Lack of feeding regimen control

Preclinical species

Higher variability in dog compared to mouse, rat and monkey, particularly at high dose

Depending on the risk for PK variability and potential impact on an in vivo study outcome (overlap in exposures between dosing groups and change to the maximum tolerated dose for a safety study, lack of clear dose response for a key efficacy experiment, etc.), a derisking strategy may be required. The strategy should be based on a preliminary assessment of risk factors described above. It can involve alterations to the study design such as an increased number of animals to better assess variability and/or a more robust control of study parameters (age, sex, body weight and in particular feeding regimen). Although not discussed here, animal pretreatment (e.g. dosing of famotidine or pentagastrin in dog) to control gastric pH for compounds with pH-dependent solubility30, 34 should also be considered. Finally, formulation selection can also be effective in mitigating variability and potential approaches include minimizing co-solvent concentration or considering amorphous solid dispersions to avoid in vivo precipitation, avoiding

in-situ salts when they have propensity for uncontrolled

precipitation in vivo, or using acidifiers to mitigate pH-dependent solubility (for basic compounds).


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PK variability can be a significant issue for both Discovery and Development programs, and an early assessment of the risk factors described in this manuscript and derisking strategy can be effective in reducing time and resources that would otherwise be involved in the conduct of multiple in vivo studies.

ACKNOWLEDGMENTS The authors would like to thank colleagues Prabha Karnachi (Structural Chemistry), Robert Saklatvala, John Higgins and W. Pete Wuelfing (Discovery Pharmaceutical Sciences) and Annette Bak for their help with data extraction, data analysis and review of the manuscript. The authors would also like to thank Shiying Chen and Fangbiao Li (Pharmacokinetics, Pharmacodynamics and Drug Metabolism) for PK support related to the case studies, as well as Dorothy Levorse and Timothy Rhodes (Preformulation) for the conduction of high-throughput solubility and log D7 measurements.



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Analyzing the Potential Root Causes of Variability of Pharmacokinetics

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