Compound Property Optimization in Drug Discovery Using

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Compound Property Optimization in Drug Discovery Using Quantitative Surface Sampling Micro Liquid Chromatography with Tandem Mass Spectrometry Xiao-Hui Chen, Panos Hatsis, Joyce Judge, Upendra A Argikar, Xiaojun Ren, Jason Sarber, Keith Mansfield, Guiqing Liang, Adam Amaral, Alexandre Catoire, Adam Bentley, Luis Ramos, Paul Moench, Samuel Hintermann, David Carcache, Jim Glick, and Jimmy Flarakos Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b03449 • Publication Date (Web): 31 Oct 2016 Downloaded from http://pubs.acs.org on November 1, 2016

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Analytical Chemistry

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Compound Property Optimization in Drug Discovery Using Quantitative Surface Sampling Micro Liquid Chromatography with Tandem Mass Spectrometry Xiaohui Chen1*, Panos Hatsis2, Joyce Judge3, Upendra A. Argikar1, Xiaojun Ren2, Jason Sarber4, Keith Mansfield3, Guiqing Liang4, Adam Amaral1$, Alexandre Catoire2, Adam Bentley2, Luis Ramos2, Paul Moench2, Samuel Hintermann5, David Carcache5, Jim Glick2 and Jimmy Flarakos2

Analytical Sciences and Imaging, 3Preclinical Safety, 4Metabolism and Pharmacokinetics, Novartis Institutes for BioMedical Research, Inc. 250 Massachusetts Ave, Cambridge , MA 02139 USA.

1

2

Drug Metabolism & Pharmacokinetics, Novartis Institutes for BioMedical Research, Inc. 1 Health Plaza, East Hanover, NJ 07936 USA. $

Present address: Drug Metabolism & Pharmacokinetics, Biogen Inc., 125 Broadway, Cambridge, MA 02142 USA

5

Global Discovery Chemistry, Novartis Institutes for BioMedical Research, Inc. Basel Switzerland

Corresponding Author *Email: [email protected] Fax: 617-871-4563 Notes The authors declare no competing financial interest.

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ABSTRACT Surface sampling micro liquid chromatography tandem mass spectrometry (SSµLCMS/MS) was explored as a quantitative tissue distribution technique for probing compound properties in drug discovery. A method was developed for creating standard curves using surrogate tissue sections from blank tissue homogenate spiked with compounds. The resulting standard curves showed good linearity and high sensitivity. The accuracy and precision of standards met acceptance criteria of ± 30%. A new approach was proposed based on an experimental and mathematical method for tissue extraction efficiency evaluation by means of consecutively sampling a location on tissue twice by SSµLC-MS/MS. The observed extraction efficiency ranged from 69% to 82% with acceptable variation for the test compounds. Good agreement in extraction efficiency was observed between surrogate tissue sections and incurred tissue sections. This method was successfully applied to two case studies in which tissue distribution was instrumental in advancing project teams’ understanding of compound properties.



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INTRODUCTION Drug discovery and development rely on characterization of a drug’s absorption, distribution, metabolism and excretion (ADME) properties, exposure-response relationship and safety profile.

The study of these properties and relationships is not possible without the

sensitivity, selectivity and throughput of techniques such as liquid chromatography/mass spectrometry (LC/MS).1 Project teams rely on bioanalytical data to facilitate decision making. However, there is a shift in recent years from data generation to data interpretation/integration.2 Decision making is most effective if people with diverse perspectives participate. It is no longer adequate to supply project teams with data, but rather contributions that fundamentally affect decision making are imperative. This requires re-examination of what bioanalytical support entails. An example is the emergence of mass spectrometry imaging (MSI)3 to facilitate analysis of drugs in tissues, which can play an important role in drug characterization.4 For example, tissue distribution can help reveal the exposure-effect relationship when the mechanism of action of a drug is associated with binding to a specific region of an organ. It is also essential in targeted delivery of drugs to determine exposure locally rather than systemically.5 Tissue analysis most commonly entails homogenization of organs, analyte extraction and LC-MS/MS analysis, and an average concentration is measured. Homogenization destroys tissue morphology, and information on spatial distribution of a compound is lost. Apart from addressing the need for tissue distribution data, MSI enables monitoring of drugs and metabolites, and is readily implemented in a drug discovery setting. This is not the case, for example, with quantitative whole body autoradiography (QWBA), which requires a radiolabeled drug, and does not differentiate between parent drug and metabolites/degradants.6 3 ACS Paragon Plus Environment

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Surface sampling micro-liquid chromatography with tandem mass spectrometry (SSµLCMS/MS) is an MSI technique based on the formation of a liquid microjunction, which facilitates extraction of analyte from the tissue surface.7 The spatial resolution of this technique depends on the size of the liquid microjunction, which is approximately 1 mm.

This resolution is

considerably lower than what is achieved with other MSI techniques, e.g., MALDI, nanospray desorption electrospray ionization or single-probe mass spectrometry imaging8-10 However, SSµLC-MS/MS promises sensitive and quantitative determination of drugs/metabolites in tissues since it is based on electrospray ionization, and it incorporates a chromatographic separation. SSµLC-MS/MS should be amenable to a wider chemical space of small molecule pharmaceutical drugs and metabolites relative to other MSI techniques since it is based on electrospray ionization.11 Quantification by LC/MS is more robust than by MALDI, since matrix effects and interferences are attenuated through chromatographic separation.

Furthermore,

SSµLC-MS/MS leverages existing instrumentation and expertise of most bioanalytical laboratories, thus enabling the support of tissue distribution studies with minimal additional resources. SSµLC-MS/MS has mostly been applied to relative quantitative analysis,12-22 however quantitative analysis by SSµLC-MS/MS is receiving increasing attention.23-25 This manuscript presents method development and implementation of quantitative tissue distribution by SSµLC-MS/MS to characterize and troubleshoot issues in drug discovery programs.

Surrogate tissue sections were used for standard curve preparation with good

linearity and high sensitivity, and accuracy and precision were within acceptance criteria. A method for the evaluation of tissue extraction efficiency by means of consecutively extracting a sampling location twice by SSµLC-MS/MS is proposed. Extraction efficiency ranged from 69% to 82%. Two case studies are presented where tissue distribution was instrumental in advancing



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project teams’ understanding of the ADME characteristics of their compounds, thus directing chemistry efforts toward further optimization of compound properties. EXPERIMENTAL Reagents and Materials. All compounds were proprietary to Novartis. Glyburide was purchased from Sigma-Aldrich (St Louis, MO, USA) and served as a generic internal standard (IS) in this work.

Ammonium acetate (99% purity) and bovine serum albumin (BSA),

lyophilized powder, were supplied by Acros (Bridgewater, NJ, USA). Blank Sprague Dawley (SD) rat brain tissues were obtained from Bioreclamation, Inc. (Hicksville, NY, USA). Isoflurane was supplied by Baxter (Deerfield, IL, USA). Carboxymethylcellulose (CMC, 800 – 3100 mPa.s) was supplied by Millipore (Bedford, MA, USA). HPLC grade acetonitrile, methanol, formic acid (88% purity), phosphate buffered saline (PBS), and dimethyl sulfoxide (DMSO) were purchased from Fisher Scientific (Bridgewater, NJ, USA). Ultra-high purity nitrogen was obtained by an in-house nitrogen system. Water was purified with a Millipore Milli-Q Gradient A10 system. Apparatus. A hand-held blender TH-1 and hard tissue disposable tips were obtained from OMNI International (Kennesaw, GA, USA). A Tecan liquid handling system (Model Freedom EVO®) was obtained from Tecan Schweiz AG (Mannedorf, Switzerland). Glass slides, 4 x 3”, were purchased from Ted Pella, Inc. (Redding, CA, USA). Cryomolds (1 x 0.8 x 0.2”) and optimal cutting temperature compound (OCT) were purchased from Sakura Finetek USA (Torrance, CA, USA). The cryostat and cryomacrotome were obtained from Leica (Nussloch, Germany). An aluminum block (2.5 x 4 x 6”) was purchased from ThyssenKrupp (Essen, Germany). A Perfection V300 Photo scanner was manufactured by Epson (Suwa, NGN Japan). All experiments were performed on an HPLC-MS/MS system, which consisted of a CTC HTC


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PAL autosampler (Zwingen, Switzerland), an Eksigent microLC 200 system and a Sciex Triple Quadruple 6500 (Concord, ON, Canada) equipped with a nano-electrospray probe (50 µm) and ionization source. Plate holders for tissue sections were purchased from Advion Inc. (Ithaca, NY, USA) Standard Surrogate Tissue Section Preparation. All test compounds were prepared at 1 mg/mL in DMSO. Working solutions were prepared by serially diluting with DMSO from primary stocks. An aliquot of 2.97 mL of blank brain homogenate was mixed with 30 µL of the desired working solution to yield brain homogenate standards ranging from 0.1 to 10000 ng/g. The blank brain homogenate was prepared by mixing varying ratios of blank SD rat brain tissue by weight with PBS by volume. The mixture was homogenized with a hand-held blender. A similar procedure was used when working solutions were prepared with 3mg/mL BSA in PBS instead of DMSO. Spiked brain homogenates were vortex-mixed for 20 seconds and transferred into a plastic cryomold, which was placed on a pre-chilled (-80 oC) aluminum block and submerged in liquid nitrogen. The bottom of the frozen homogenate was attached to the cryostat chuck with OCT. Sections with a nominal thickness of 20 µm were generated using a cryostat and were placed in the center of a glass slide, and dried at room temperature. Only the upper most sections of the frozen homogenate were used in experiments, to avoid the presence of OCT in surrogate tissue sections.

Although OCT is commonly used in tissue section preparation,8,26 initial

investigations revealed its use was not ideal for the purposes of this work. Bleeding of OCT into the tissue was observed resulting in a vague boundary, which made it difficult to distinguish tissue from OCT. There were concerns about potential matrix effects and interferences, as well



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as false negative results especially along the boundary between tissue and OCT. As a result, the procedure outlined above was developed to circumvent these issues. Two separate surrogate tissue sections were used during initial method development with Compound A. Multiple, non-overlapping points (21 on average, each of approximately 1 mm diameter) were sampled across the entire area of each tissue section and at each concentration for SSµLC-MS/MS analysis. The average value of the peak area ratio of IS at each concentration was used to construct a standard curve. The acceptance criteria for accuracy and precision of all standard curves were taken from the Food and Drug Administration guidance on bioanalytical method development, but were expanded to ± 30%, which is accepted practice in the industry for tissue matrices in a drug discovery setting.27 Robustness and Reproducibility of the Quantification Method.

Blank brain

homogenate was prepared with rat brain tissue and PBS at a ratio of 1:1, followed by spiking in Compound B and C at 7 concentrations from 0.5 to 500 ng/g. Three spots were randomly selected from the resulting surrogate tissue sections and analyzed by SSµLC-MS/MS. A standard curve was constructed using the average peak area ratio of analyte to IS. SSµLC-MS/MS Analysis. The SSµLC-MS/MS system consisted of four major components: (1) a conventional flatbed scanner to generate optical images of tissue sections, (2) a CTC HTC PAL autosampler to perform liquid-solid extraction on the tissue surface followed by sample injection, (3) an Eksigent microLC 200 liquid chromatography system coupled with (4) a Sciex Triple Quadruple 6500 mass spectrometer to separate and analyze the materials extracted from the tissue surface. This integrated system was controlled using LMJ Points Plus© (Oak Ridge National Laboratory, Oak Ridge, TN) and Sciex Analyst (v1.6.2) software. LMJ Points Plus© software was used for importing scanned images of tissue sections, image co-ordinate 7 ACS Paragon Plus Environment

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calibration, selecting tissue sampling locations from the scanned tissue image,

and batch

creation. This information was then transferred to Analyst which was used for data acquisition. Data analysis and visualization was performed in LMJ Points Plus©. SSµLC-MS/MS analysis involved aspiration of 3 µL of extraction solvent (50/50 acetonitrile/water containing 50 ng/mL of glyburide as IS) into an autosampler syringe, followed by movement of the syringe to the desired sampling spot. The syringe was then lowered to a predefined distance (100–300 µm) above the sampling spot, and the extraction solution was dispensed onto the tissue to create a liquid microjunction between the needle and the surface. After a predefined extraction time of one second, the liquid was then aspirated back into the syringe, which completed one extraction cycle. This process was repeated twice after successive one-second delays. Upon completion of all extraction cycles, the syringe moved to the autosampler injection port and injected the extract for LC-MS/MS analysis. Several steps were taken to ensure the reliability of surface sampling. For example, the instrument and plate holders were levelled before analysis. In addition, all extraction solutions and sampling parameters were optimized for each test compound to form symmetrical liquid junctions without bubbles, and to achieve the desired sensitivity, extraction efficiency, and sampling reliability.28,29 LC-MS/MS Analysis. Chromatographic separations employed an Acquity UPLC BEH C18, 1.7 µm, 50 x 1.0 mm i.d. analytical column (Waters, Milford, MA USA), and gradient elution employing two separate solvent systems, depending on the application. One solvent system used mobile phases consisting of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The second solvent system used mobile phases consisting of 10 mM ammonium acetate in water (A) and 10 mM ammonium acetate in acetonitrile (B). Following sample injection of 3 µL, the mobile phase composition was held at 2% B for 0.5 minutes. It was then 8 ACS Paragon Plus Environment


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increased linearly to 98% B over 1.5 minutes, and then held at 98% B for another 0.5 minutes. This was followed by re-equilibration of the column at the initial mobile phase conditions. The flow rate was 60 µL/min, and the column was maintained at ambient temperature. LC-MS/MS was performed in positive (5500 V) or negative polarity (-4500 V) depending on the test compounds. The source temperature was 550 oC. Gas 1 and Gas 2 settings for nitrogen were set to 20 and 15 respectively. The curtain gas and collision gas were both nitrogen and were set to 30 and 10 respectively (arbitrary units). All data was acquired using selected reaction monitoring (SRM) transitions with dwell times of 50 milliseconds. All data were acquired and quantified using Analyst® software. MS data was then converted by LMJ Points Plus© Software to a heatmap. Extraction Efficiency Measurement. The extraction efficiency of SSµLC-MS/MS was evaluated by sampling the same location twice using three aspirate-dispense cycles each time. Assuming distribution equilibrium is maintained during both extractions:30

K=

R1 R2 = x − R1 x − R1 − R 2

(1)

Solving for x gives: R12 x= R1 − R 2

(2)

The extraction efficiency of surface sampling for the first sampling is given by:

(%) =

∗ 100

Substituting Equation 2 in Equation 3 gives:


(3)

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(%) =

∗ 100

(4)

where K is the equilibrium constant for extraction, x is the hypothetical total peak area ratio of analyte in the sampling location, R1 and R2 are the peak area ratio of analyte to IS from the first and second sampling, respectively. This approach required that each sampling was performed on a dry tissue surface, as a wet tissue surface may cause higher extraction efficiency. Therefore, an hour was allowed for the tissue surface to dry between successive sampling, i.e., the time interval between the first and second sampling. The extraction efficiencies reported in this investigation were the average values from 3 to 6 spots from standard tissue sections at low, medium and high concentrations. New sampling locations were used for SSµLC-MS/MS analysis in method development and case studies for reasons of consistency.

Incurred Tissue Section Preparation in Case Study 1. Compound D was infused intravenously (0.205mg/kg/hr, 1.2ml/kg/hr) in SD rats for 5 hours to reach steady state. The rats were then sacrificed and the brain was harvested. Average brain concentration was obtained by analyzing one half of the brain using conventional tissue homogenization followed by LCMS/MS. Plasma samples at 5 hours were quantified with a plasma standard curve by LC-MS/MS. The other half of the brain was frozen and 8 sections, in the sagittal plane across the hemisphere were prepared by the method described in the section Standard Surrogate Tissue Section Preparation. Approximately eight to twenty sampling locations were selected from 8 sections such that there was no overlap between neighboring sampling locations. These tissues were analyzed by SSµLC-MS/MS.

Incurred Tissue Section Preparation in Case Study 2. Compound P1 was administered orally to rats at 50 mg/kg. Animals were euthanized 4 hours post dose using deep isoflurane



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anesthesia followed by freezing in a -70°C hexane/dry ice bath. The animals were shaved and mounted on metal stages in cold 2% (w/v) CMC. Specimen blocks were maintained at or below 20°C at all times. Tissue sections (~4-5/animal) were prepared using a cryomacrotome, and mounted on glass slides using double-sided tape. This was necessary to ensure high quality mounting due to the relatively large size of whole-body tissue sections in this case study. The sections were desiccated in the cryomacrotome for at least 24 hours at -20°C. Once drying was completed, sections were maintained at ambient temperature until SSµLC-MS/MS analysis. Standard tissue sections were prepared by spiking in both Compound P1 and its metabolite, Compound M into blank brain homogenate at concentrations ranging from 0.5 to 500 ng/g. Whole-body tissue samples were analyzed by sampling across the entire area of most organs including heart (3), thymus (3), lung (6), liver (15), gastro intestinal tract (GI) (27), and the fat tissue around the GI tract (9). The numbers in parentheses indicate the number of sampling locations in each organ with no overlap between neighboring locations. A follow-up study was performed on a modified analog of Compound P1, Compound P2, which was dosed orally to rats at 10 mg/kg, using the same study design and methods described above. Whole-body sections were prepared at 2 hours post dose and multiple locations were selected across most organs including heart (2), thymus (3), lung (3), liver (6), GI tract (12) and the fat tissue around the GI tract (5), depending on the size of the organs. Compound P2 and the identical metabolite, Compound M, were analyzed by SSµLC-MS/MS. Tissue concentrations were dose standardized to facilitate comparison to Compound P1. This was accomplished by dividing each individual concentration value of Compound P1 and M from the initial experiment by 5, so that all the tissue concentrations corresponded to the same dose of 10 mg/kg.


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RESULTS AND DISCUSSION MSI, e.g., MALDI, has been mostly applied to relative quantification of drugs in tissues.31-39 The heterogeneous nature of ionization and complex tissue environment make absolute quantitative analysis with MSI challenging. This is an active area of research,40-48 and different approaches for quantification have been reported.49-51 Reference standards may be spotted or sprayed on blank tissue sections. This approach requires careful control and verification of the evenness of penetration through tissue sections. It may be challenging to obtain homogeneous concentrations across the tissue surface depending on the spraying method, in addition to the uncertainty in solvent evaporation. These may have a direct impact on the accuracy and precision of quantification, and accurate reflection of extraction efficiency of analytes from incurred tissue samples.8 An alternative strategy which is used in this investigation involves the use of brain tissue homogenate to prepare surrogate tissue sections. A similar strategy was reported previously, but for MALDI-MSI.8,26 The purpose of this investigation was to develop a method of surrogate tissue section preparation that is robust and reliable, and complements the inherent sensitivity of electrospray ionization for small molecules. This enabled tissue distribution determination at therapeutically relevant doses, rather than requiring experiments performed at increased doses to accommodate the limited sensitivity of the method.

The details of the development and

optimization of the method for surrogate standard tissue preparation are discussed below.

Optimization of Standard Surrogate Tissue Section Preparation.

Most of the

standards for Compound A failed the acceptance criteria (data not shown) when a 1:2 ratio of blank brain homogenate to PBS was used. Moreover, the observed spot-to-spot variation of signal response was as high as 150% (% CV).



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Careful inspection revealed that the surface of surrogate tissue section standards had inconsistencies such as fine lines. In order to more closely mimic incurred tissue samples in terms of composition and density, the ratio of brain tissue to PBS was modified to 1:1. Perusal of Table 1 reveals that surrogate tissue sections were more homogeneous, and acceptance criteria were achieved for 6 out of the 7 standards. The lower limit of quantification (LLOQ) was 5 ng/g for Compound A. Spot-to-spot variation of signal response was also significantly improved (below 20% for 6 out of 7 concentrations). The LLOQ was further improved by spiking BSA into tissue homogenate. Since tissue homogenates were prepared in aqueous solution without addition of organic solvent, proteins and/or lipids, analytes may potentially non-specifically adsorb to containers, and/or precipitate out of solution, which may adversely affect quantification. As indicated in Table 2, the addition of BSA greatly improved the quality of tissue sections and extended the linear range of the standard curve on the lower end. All standards passed the acceptance criteria. The LLOQ of Compound A was lowered from 5 ng/g to 0.5 ng/g.

Robustness and Reproducibility of the Quantification Method. It is imperative that standard curves meet the desired acceptance criteria, but it is also important to generate a robust data set with acceptable reproducibility regardless of the location and number of tissue sampling points. This was examined by re-constructing two standard curves using data derived from the tissue sections illustrated in Table 1. Three points at each concentration were selected from either the same section or from two sections having the same concentration, by two separate operators in a random and blinded fashion. Their chosen sampling points were compiled to re-construct two more standard curves which were compared with the original standard curve using all sampling locations. All three sets of curves were equivalent in terms of their slope and intercept


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(data not shown). The correlation coefficients for the re-constructed curves were greater than 0.98 while the original correlation coefficient was greater than 0.99. This demonstrated that the tissue sections prepared by the method described above were truly homogeneous so that tissue concentrations could be determined by randomly sampling as few as three spots. To further confirm the robustness of this method, Compound B and C were tested by using three points at each concentration. Acceptance criteria were achieved with good linearity (r2 greater than 0.98 for both compounds). The LLOQ was 0.5 ng/g for both compounds (S/N = 10). This experiment offered further confirmation that standard surrogate tissue sections could be reproducibly prepared by this method. The resulting standard curves were linear with good reproducibility regardless of sampling location or frequency. Moreover, by sampling only three spots at each concentration, the time required for data acquisition was significantly reduced.

Extraction Efficiency of Surface Sampling. Extraction efficiency can be an indicator of standard surrogate tissue section homogeneity, and of comparability to incurred tissue sections. Extraction efficiency must be precise, reproducible, and sufficient for achieving desired sensitivity. However, unlike bioanalysis of liquid matrices, evaluation of extraction efficiency from tissue sections is not straight forward.

A new approach was proposed based on an

experimental and mathematical method for tissue extraction efficiency evaluation by means of consecutively sampling a location on tissue twice by SSµLC-MS/MS. The observed extraction efficiency ranged from 69% to 82% with acceptable variation for the test compounds. Table 3 shows an example of the average extraction efficiency (82%) and CV% (15%) measured for Compound D at low, medium and high concentrations. The efficient extractions give additional support for the high sensitivity that was observed with SSµLC-MS/MS analysis. The low variation in extraction recovery demonstrated that the developed method results in homogeneous



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concentrations across different sections from different preparations and across a wide concentration range (1 – 500 ng/g). Extraction efficiency was compared between brain surrogate tissue sections and incurred brain sections. The average extraction efficiency of Compound D was 82% from surrogate tissue sections (Table 3). Table 4 shows the extraction efficiency and CV% measured from one of the incurred brain tissue sections. Ten locations on the section, with concentrations ranging from 4 to 362 ng/g, were consecutively extracted and analyzed. The resulting average extraction efficiency from the incurred tissue sections was 67%. The relative error was only 22%, which is within the predefined acceptance criteria for this methodology, as well as for conventional tissue analysis. Therefore, this suggests that the difference in assayed concentrations between surrogate brain standard sections and incurred tissue sections is acceptable, and surrogate brain standard sections adequately represented incurred brain sections in terms of extraction and matrix effects of the spiked drug. The method proved to be appropriate for the scope of the work performed in a drug discovery setting.

Case Study 1. Compound D has optimal solubility, permeability, cell potency, and affinity to the intended target. However, its ability to penetrate the blood-brain barrier to achieve adequate brain exposure was questionable due to its large molecular weight (905 g/mol). The brain-to-plasma ratio was previously measured by conventional bioanalysis of brain tissue and plasma sampled from a rat at the same time point post dose. The result was inconclusive because the ratio was on the margin of blood contamination, i.e., 0.05. Further investigation was performed by an IV infusion experiment to assess the possibility of Compound D to penetrate the blood-brain barrier. The plasma concentration measured by LC-MS/MS at 5 hours post-dose was


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515 ng/g, and the average brain concentration by conventional tissue bioanalysis was 41 ng/g. The brain-to-plasma ratio of the compound was 0.08, which was consistent with previous results. The other half of the brain was probed using SSµLC-MS/MS. The correlation coefficient of the standard curve for Compound D was 0.99, the LLOQ was 0.5 ng/g and acceptance criteria were met. The concentration of Compound D in brain was found to be low throughout most regions of the section. However, concentrations as high as 650 ng/g, were observed in certain regions, which was much higher than plasma concentration at the same time point. SSµLCMS/MS provided clear, although somewhat surprising evidence that Compound D penetrated into the brain in spite of its large molecular weight. The project team focused on other possible factors to explain the low brain-to-plasma ratio, and identified efflux as a potential cause. A transporter knock-out mouse model was used to probe brain exposure. The brain exposure of Compound D in this model was greatly increased, and the average brain to plasma ratio determined using conventional methods was increased from 0.08 to 5. This study highlights the importance of determining tissue distribution, and how it can influence decision making in a drug discovery environment when used strategically.

Case Study 2. The utility of the surrogate brain tissue quantification method was demonstrated in the previous case study. In the second case study, SSµLC-MS/MS was applied to whole body tissue section analysis. Compound P1 has compelling ADME properties. However, its bioavailability was low (~15%) in spite of low clearance. Moreover, Compound M (a metabolite), was observed when Compound P1 was dosed orally to rats at 50 mg/kg.

A SSµLC-MS/MS experiment was

designed to elucidate the mechanism of the low oral bioavailability. The correlation coefficients



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of the standard curves for Compound P1 and Compound M from this study were 0.99 and 0.98, the LLOQs were 1 and 5 ng/g, respectively and acceptance criteria were met for both compounds. The average extraction efficiency for Compound P1 and M was 71% and 69%, respectively. The whole body section was analyzed using SSµLC-MS/MS, and the measured concentration values were overlaid with the scanned image of the tissue samples to create colorcoded heatmaps. These are shown in Figure 1 to illustrate the tissue distribution of Compound P1(left) and Compound M (right) across the middle of the whole body section, respectively, at 4 hours post dose. A rainbow bar using a logarithmic scale (% relative concentration) reflects the difference in concentration, where red indicated the highest, and black indicated the lowest concentration. The results showed that both Compound P1 and M were observed predominantly in the small and large intestine at extremely high concentrations. Exposure was much lower in all other organs. For example, the average liver concentration from all sampling positions was 660 ng/g for Compound P1 and 180 ng/g for Compound M, while the average GI tract concentration (n=27) was about 120000 ng/g for Compound P1 and 34000 ng/g for Compound M. The concentrations of both compounds in the GI tract were above the upper limit of quantification. This issue could have been addressed by performing online post-extraction dilution before injections. However, GI concentrations were approximately 200-fold higher than in other organs, rendering the determination of absolute GI concentrations much less important to the conclusions of the experiment (see below). Therefore, all concentrations reported in this experiment were calculated and/or extrapolated from the linear standard curves. The parent compound and its metabolite showed highly localized distribution in the GI tract, compared to other organs, 4 hours post dose. Instead of being absorbed, Compound P1


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remained in the GI tract and degraded. Poor oral absorption, most likely due to low solubility and poor chemical stability of a benzylic ether moiety present in Compound P1 at low pH, may be responsible for its low bioavailability in rat. Based on these findings and the derived hypothesis, the project team focused its efforts on improving stability of Compound P1. Incorporation of heteroatoms into the benzylic ether moiety lowered the log P from 5.3 to 3.1, resulting in improved in-vitro solubility and chemical stability. A follow-up study was performed on the modified compound, Compound P2, and its identical metabolite, Compound M. The standard curves for both compounds were linear (r2 greater than 0.98 for both compounds), the LLOQs were 1 and 5 ng/g, respectively and the acceptance criteria were met for both compounds. Figure 2 compares the tissue distribution (based on dose standardized concentrations) of Compound P1 (top) and P2 (bottom) and their common metabolite, Compound M in seven organs. The concentrations in the charts were the average values from all sampling locations for each organ. The results clearly indicated that Compound M derived from Compound P2 was not detected in heart, lung, and thymus, and was much less in GI tract compared to Compound P1. The ratio of Compound P1 to its derived Compound M in GI tract was approximately 4, whereas the ratio of Compound P2 to its derived Compound M in GI tract was approximately 240, indicating that the formation of Compound M from Compound P2 was approximately 60-fold less than Compound P1. The improvement in stability allows Compound P2 to better survive the GI tract, and possibly absorb more through the intestine, resulting in higher oral bioavailability,



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which was confirmed in a follow-up pharmacokinetic study (increased to ~60%). On the other hand, the high concentration of Compound P1 in the GI tract clearly shows that this compound is at risk of poor oral absorption.

Assumptions and Future Direction. The case studies have demonstrated the utility of assessing tissue distribution using SSµLC-MS/MS to contribute to decision making in a project team. It is important to point out the inherent assumption in Case Study 2, i.e., that brain surrogate tissues can be used to quantify exposure in a variety of different tissues. However, the differences in cellular structure, composition and hydrophobicity between different types of tissue may yield different analyte extraction efficiencies and matrix effect, albeit the latter should be minimized with the chromatographic separation step. This assumption was made based on previous work in the authors’ laboratories,29 to expedite the turnaround of results for the second case study, and in keeping with the fit-for-purpose philosophy for studies in the discovery setting. Further studies are being carried out in the authors’ laboratories to understand differences in extraction efficiency across tissue types, and if this information can be used to develop normalization factors to compensate for these differences.24 The spatial resolution for SSµLC-MS/MS is currently limited to about 1 mm. This parameter is dependent on the composition and volume of extraction solution used, the texture and homogeneity of the tissue sections and the contact angle of the extraction solution on the surface.28 Although spatial resolution is low compared to other MSI techniques, e.g., MALDI (< 100 µm), which may limit its application in certain instances, SSµLC-MS/MS is still a powerful profiling tool in drug discovery, particularly when ease of use, limited sample preparation, quantification and sensitivity that complements therapeutically relevant dose levels are imperative. Overall, as demonstrated in this investigation, SSµLC-MS/MS is an extremely


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useful tool to understand differential tissue distribution and the generated data is directly applicable

to

develop

concentration-effect

relationships

and

dynamic

models

like

physiologically-based pharmacokinetic models. Improvements in resolution, when combined with the distinct advantages of SSµLC-MS/MS, should enable wider acceptance and implementation in the pharmaceutical industry.



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ACKNOWLEDGEMENTS The authors would like to thank George Marsh, Suzie Ferreira, Irina Vinar and Harvey Chin from Novartis, as well as Vilmos Kertesz and Gary van Berkel of Oak Ridge National Laboratory for their helpful scientific discussions and technical assistance.


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Figure 1 SSµLC-MS/MS heatmap of tissue distribution of Compound P1 (left) and Compound M (right) in rat at 4 hours after a single oral dose at 50 mg/kg. The % relative concentration is indicated by the rainbow. Fat around GI Muscle Lung

Thymus Heart Liver GI tract



Figure 2 Tissue distribution (in terms of dose-standardized concentrations) of Compound P1 (on top) and P2 (on bottom), and their common metabolite, Compound M in seven organs.

Compound P1 and Derived Compound M

Dose Standardized Concentration (ng/g)

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Page 28 of 30

Compound P2 and Derived Compound M


Page 29 of 30

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Table 1 Accuracy and precision of tissue standard curve prepared with a ratio of tissue to PBS of 1:1. Brain : PBS = 1:1 (w/v)

Compound A Concentration (ng/g)

Measured Conc. (ng/g)

Accuracy %

Number of Sampling Locations (n)

CV %

1 5 10 50 100 500 1000

1.7 4.6 12 44 110 520 920

170 91 120 89 110 100 92

12 24 24 26 20 20 18

10 16 16 33 15 19 19

Table 2 Accuracy and precision of tissue standard curve prepared with a ratio of tissue to PBS of 1:1 and BSA at 3 mg/ml Brain : PBS = 1:1 (w/v) with BSA at 3 mg/ml

Compound A Concentration (ng/g)

Measured Conc. (ng/g)

Accuracy %

Number of Sampling Locations (n)

CV %

0.1 0.5 1 5 10 50 100

0.25 0.44 1.2 5.9 9.8 43 90

250 88 120 120 98 86 90

22 24 26 23 18 25 22

140 18 13 20 11 21 14



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Table 3 Extraction efficiency and CV% measured from the standard surrogate tissue sections at three concentrations for Compound D Compound D Conc. (ng/g) 1

50

500

Area Ratio R1 0.0061 0.022 0.013 0.10 0.13 0.12 3.4 0.88 1.3

R2 0.0019 0.010 0.0017 0.016 0.020 0.017 0.15 0.14 0.13

Extraction Efficiency (%) Individual Mean CV % (n=9) 69 54 87 84 85 82 15 86 96 84 90

Table 4 Extraction efficiency and CV% measured from an incurred brain tissue section harvested at 5 hours post dose with Compound D in rats Sample ID Brain section 4-1 Brain section 4-2 Brain section 4-3 Brain section 4-4 Brain section 4-5 Brain section 4-6 Brain section 4-7 Brain section 4-8 Brain section 4-9 Brain section 4-10

Area Ratio R1 0.035 1.1 0.025 0.022 0.022 0.015 0.024 0.057 0.021 0.034

Extraction Efficiency (%) Individual Mean CV % (n=10) 23 84 66 72 69 67 25 68 63 72 74 78

R2 0.027 0.18 0.0084 0.0061 0.0068 0.0048 0.0089 0.016 0.0055 0.0075


Page 31 of 30

For TOC only

Tissue sections Surface sampling (SS) X IC of +MRM (3 pa irs): 46 6. 252/35 1.000 Da from Sample 2 41 (K3 21_QC08 _2_I1) of K 32 1.wiff (Turb o Spra y) I n t e

Max. 7.9e 5 cp s.

7.9e5 7.0e5 6.0e5

n s i t y , c

5.0e5 4.0e5 3.0e5 2.0e5

p 1.0e5 s 0.0 0.0 0.2 0. 4 0.6 0.8 1 .0 1.2 1.4 1. 6 Time, min X IC of +MRM (3 pa irs): 49 4. 200/16 8.800 Da from Sample 2 41 (K3 21_QC08 _2_I1) of K 32 1.wiff (Turb o Spra y)

I n

1.8

2.0

2 .2

2.4

2.6

2.8 Max. 1.0e 6 cp s.

1.00e 6 9.00e 5

t e

8.00e 5

n

7.00e 5

s

6.00e 5

i t y

5.00e 5 4.00e 5

, 3.00e 5 c p

2.00e 5

s

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


1.00e 5 0.00 0.0

Heatmap of tissue distribution

0.2

0. 4

0.6

0.8

1 .0

1.2

1.4 1. 6 Time, min

1.8

2.0

2 .2

2.4

2.6

2.8

µLC-MS/MS with ESI


Compound Property Optimization in Drug Discovery Using

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