Early Absorption and Distribution Analysis of Antitumor and Anti

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Early Absorption and Distribution Analysis of Antitumor and Anti-AIDS Drugs: Lipid Membrane and Plasma Protein Interactions Samanta Cimitan,†,‡ Maria T. Lindgren,† Carlo Bertucci,‡ and U. Helena Danielson*,† Department of Biochemistry, Uppsala University, Box 576, SE-751 23 Uppsala, Sweden, and Department of Pharmaceutical Sciences, Bologna University, Via Belmeloro 6, 40126 Bologna, Italy Received August 10, 2004

The interactions of a set of compounds of potential importance for anticancer and AIDS chemotherapy with lipid membranes and plasma proteins were studied with a surface plasmon resonance (SPR) based optical biosensor, giving valuable information on the absorption and distribution of the compounds. The technique allowed both effective screening of compounds and more detailed kinetic and mechanistic analysis of specific interactions. The interaction with two different types of lipid membranes could be reliably measured at a drug concentration as low as 20 µM, allowing analysis of poorly soluble compounds. Distribution was evaluated by investigation of the interactions with two human plasma proteins, human serum albumin (HSA) and R1-acid glycoprotein (AGP). Two apparent binding sites were clearly defined for HSA: one with rapid and one with slow association and dissociation rates. The sites appear to differ in accessibility and recognition characteristics rather than in their capacities to form strong complexes with drugs. Introduction Efficient and accurate determination of the effect of potential drug leads on their targets is critical for the development of potent drugs. This is reflected in the recent expansion of high-throughput screens for a variety of targets.1 However, before a drug interacts with its target, it has to cover a long and complex journey within the human body. Biological activity is thus influenced by other factors such as absorption, distribution, metabolism, and excretion (ADME).2 Unfortunately, efficient methods for screening of potential drug compounds for such characteristics are not generally available, and existing methods have low capacity and/or high cost.3 These important features are typically only determined for the most promising compounds, late in the drug-development process. However, there is an increased understanding that these properties should be investigated in the early stages of the drug discovery process because advantageous properties may be lost upon optimization.4 A recent strategy has been to use in silico methods for prediction of ADME properties.5 They are based on parametrization of the chemical structures, physicochemical data, and preferably also experimental data. The limited success of these methods is partially due to the restricted data used as input for the modeling. Therefore, increasing the availability of ADME data for modeling is of importance for an early rational selection of lead compounds. On the basis of these fundamental considerations, the current study focuses on determination of experimental data for two characteristics related to ADME: absorption and distribution. Since an overwhelming majority of new drugs are intended to be administered orally, good oral bioavailability is imperative. It can be determined relatively * To whom correspondence should be addressed. Phone: +46 18 471 4545. Fax: +46 18 558431. E-mail: helena.danielson@ biokemi.uu.se. † Uppsala University. ‡ Bologna University.

easily by pharmacokinetic studies in animals, but for economical and ethical reasons, it is not reasonable to expect that this approach will be adapted to large-scale screening of compounds.6 Instead, a number of in vitro tools have been adapted to high-throughput assessments of membrane permeability from which oral bioavailability is predicted.7,8 For example, methodologies based on the use of liposomes, immobilized artificial membranes (IAM), and CaCo-2 cells are currently used for getting absorption data. Drug distribution is greatly influenced by binding of drugs to plasma proteins. Human serum albumin (HSA) and R1-acid glycoprotein (AGP) appear to be the most important plasma proteins with pharmacokinetic implications.9 HSA and AGP both bind a wide variety of drugs and a number of relatively insoluble endogenous and exogenous compounds, thus facilitating their transport throughout the body. On the other hand, high affinity for plasma proteins reduces the free concentration of the drug in the blood stream. Depending on the drug and the target, high affinity for plasma proteins may consequently be an asset or a drawback for efficacy. HSA is the most abundant protein in blood plasma and serves as a transport protein and a depot for numerous compounds, especially hydrophobic compounds. The binding of lipophilic drugs to this protein results in improved solubility and sometimes in decreased toxicity.10 Albumin concentrations may decrease upon illness, and its capacity to bind a certain compound is dependent on the conditions (pH, temperature, and ionic strength) and competition with other compounds (for a review, see Nicholson et al.11). AGP is often considered to be the most important acute phase protein. Its serum concentration increases in response to systemic tissue injury, inflammation, or infection, and these changes have been correlated with increases in hepatic synthesis of AGP (for a review, see Fournier et al.12). Variations in plasma levels of AGP occurring during inflammatory processes can considerably alter

10.1021/jm049343o CCC: $30.25 © 2005 American Chemical Society Published on Web 04/15/2005

ADME Studies Using Biosensor Technology

Journal of Medicinal Chemistry, 2005, Vol. 48, No. 10 3537

Figure 1. Chemical structures of compounds analyzed: (a) taxanes: docetaxel (dtx), paclitaxel (ptx), ortataxel (otx), IDN5390 (seco), 7-hemisuccinylpaclitaxel (7sptx), 14-β-hydroxy-baccatin III 1,14-carbonate (cdab), 10-deacetylbaccatin III (dab), 7-biotinylpaclitaxel (bptx); (b) HIV protease inhibitors: amprenavir (amp), indinavir (ind), saquinavir (sqn), nelfinavir (nlf), atazanavir (atz), lopinavir (lpn), ritonavir (rtn).

the free plasma concentration of the drug without affecting its total plasma concentration. The protein binding of antiretroviral drugs is an important issue that lacks standard evaluation procedures (for a review, see Boffito et al.13).

The major goal of this work was to develop efficient methods that can provide experimental data that increase the understanding of the biological activity of anticancer and AIDS chemotherapy drugs. Anticancer drugs were represented by eight taxol-like compounds

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(Figure 1a). These lead to tumor cell death by stabilizing microtubules, thereby preventing cell mitosis.14 The anti-AIDS compounds comprised seven HIV-1 protease inhibitors used in the clinic (Figure 1b).15,16 There are no indisputable absorption or distribution data available in the literature with regard to this set of molecules.17-20 The bioavailability data we have access to often suffer from discrepancies and great variability from assay to assay. This is likely due to the diverseness of the experimental parameters sought and also to the complex pharmacokinetics and pharmacodynamics of these classes of molecules. The interactions between lipid membranes, HSA (human serum albumin), and AGP (R1-acid glycoprotein) with taxanes and HIV-1 protease inhibitors were studied using a surface plasmon resonance (SPR) based biosensor.21,22 Optical biosensor methodology has recently been adopted for studies of a variety of interactions between low molecular weight compounds and several drug targets. In particular, a series of studies have been performed with HIV-1 protease inhibitors.23-27 Biosensor-based analyses of drug interactions with lipid membranes28-30 and HSA31-34 have indicated that these types of measurements are also feasible, although the experimental strategies and analyses have varied. A recently developed surface plasmon based biosensor instrument (Biacore S51) was used in order to maximize the sensitivity and the throughput of the analysis, allowing an experimental design that could efficiently give relevant information about the set of compounds of interest and that could be set up for screening applications. Furthermore, this technology allows simultaneous monitoring of drug interactions with several different proteins or lipid mixtures. Results 1. Lipid Membrane Interactions. The devised method resulted in sensor surfaces suitable for the drug-lipid membrane interaction studies with taxanes and HIV protease inhibitors. By capture of fresh liposomes before each sample injection, contamination from previously injected samples was minimized. Preparation of lipid membrane surfaces was highly reproducible, and the consumption of liposomes was low. Initially, screening was performed with 150 µM analyte, a significantly lower concentration than that used in the primary lipid membrane study using this technique (500 µM).28 However, many of the compounds used in this study were not soluble even at this concentration, so screening was also performed at 20 and 100 µM. Since there were only minor differences in the ranking of the compounds at the different concentrations, screening with lower concentrations was preferred because it reduced sample consumption and allowed analysis of poorly soluble compounds. Two types of lipid membranes were used for interaction studies, one consisting of a single lipid with a zwitterionic surface (POPC) and one consisting of a mixture of three different synthetic phospholipids (DOPE/ DOPS/DOPC) resulting in a slightly negatively charged surface. The binding of the taxane compounds to a DOPE/DOPS/DOPC lipid membrane surface is illustrated in Figure 2. Association and dissociation rates were generally rapid, but the steady state levels were not stable. A simple binding model could therefore not describe the sensorgrams, and kinetic constants could

Cimitan et al.

Figure 2. Sensorgrams for interactions between taxane analogues (20 µM) and a DOPE/DOPS/DOPC lipid membrane surface.

not be determined from the experiments. Instead, the maximum binding response after injection of the compound for 60 s was determined for each compound and values were expressed as the fractional binding relative to propranolol. When interactions were normalized relative to propranolol and when famotidine was set as a low reference point, the binding of the tested compounds was easily visualized relative to these two reference compounds. Scatter plots of the relative binding to both lipid membrane surfaces (Figure 3) showed that the interaction with the two surfaces was correlated. When all compounds were included, the correlation coefficient (r2) was 0.75. Ceftriaxone, chosen as a negative control, showed little interaction with the lipid membranes, consistent with its highly polar structure. All taxanes, except 7-biotinylpaclitaxel, and HIV protease inhibitors showed higher responses to both membranes than famotidine. In addition, four of the taxanes and HIV protease inhibitors had higher response levels for at least one of the membrane types than propranolol: ortataxel, docetaxel, saquinavir, and nelfinavir (located in the upper right corner of the graph). 1.1. Taxanes. A comparison of the lipid membrane response levels of the taxane compounds (9 in Figure 3a) showed that there were large differences within this group of compounds. The modifications of the precursors designed to increase bioavailability generally resulted in increased responses in the lipid membrane assay. Interestingly, the natural precursor always showed a lower response level than the drug itself; this applied to 10-deacetylbaccatin III, which exhibited a lower response with respect to docetaxel and paclitaxel, and to 14β-hydroxybaccatin III 1,14-carbonate, the natural precursor for ortataxel, which again showed a lower response level. Moreover, the taxane derivatives designed with the aim of achieving both higher water solubility and better bioavailability displayed significantly higher liposome response levels than paclitaxel. This was the case for the synthetic C-seco taxoid, the more water-soluble 7sptx analogue of paclitaxel35 and ortataxel. 1.2. HIV-1 Protease Inhibitors. There were also large differences in the response levels for the interactions between HIV-1 protease inhibitors and the membranes (b in Figure 3b). Saquinavir and nelfinavir showed the highest response levels for both membranes,



Figure 3. POPC and DOPE/DOPS/DOPC lipid membrane interactions with taxane derivatives (9, part a), HIV-1 protease inhibitors (b, part b) and reference compounds: ceftriaxone (cfx), propranolol (prp), hydrochlorothiazide (hct), famotidine (fmt) (2). Each data point represents the binding signals (RU) after injection of 20 µM of each compound for 60 s, corrected for nonspecific interactions with the reference surface and for differences in the refractive indexes between solvent and sample. The signal was divided by the molecular weight of the compound, giving the final unit RU/Da. The data were also normalized against the positive control propranolol, set to 100 RU/Da for both liposome types. The graph has been divided into regions of low, intermediate, and high binding responses to both surfaces using the responses for propranolol and famotidine as reference points.

Figure 4. HSA and AGP interactions with HIV-1 protease inhibitors (b, part a), taxane derivatives (9, part b), HSA reference compounds warfarin (wrf), naproxen (npr), digitoxin (dgt), diazepam (dzp), quinine (qun) ([), and AGP reference compounds dipyridamole (dpy), alprenolol (alp), propranolol (prp), pindolol (pnd) (+). Each data point represents the binding (RU) of each compound at 30 µM to HSA and AGP, corrected for nonspecific interactions with the reference surface and for differences in the refractive indexes between solvent and sample. Data were normalized against the positive control naproxen for HSA and dipyridamole for AGP, both set to 100 RU/Da. The graph has been divided into regions of low, intermediate, and high binding responses to both proteins using the responses for pindolol and dipyridamole as reference points.

considerably higher than the reference propranolol. As a matter of fact, the two compounds share a common structural feature, the (S,S,S)-decahydroisoquinoline3-carbonyl group, possibly contributing to the high membrane response. However, a reliable correlation requires investigation of a larger number of compounds. Amprenavir had a lower response level but not as low as indinavir, atazanavir, lopinavir, and ritonavir, which all had similar interaction characteristics for the membranes. 2. Plasma Protein Interactions. 2.1. Screening. Compounds were injected over both plasma protein surfaces at a fixed concentration of 30 µM. A scatter graph displaying the binding levels of the tested compounds to both proteins (Figure 4) illustrates that there was a correlation between their interaction to HSA and

to AGP. Several compounds with known HSA binding characteristics were selected as references ([ in Figure 4), ranging from rac-naproxen (99.9% bound fraction) to digitoxin (34% bound fraction).36 Dipyridamole, propranolol, alprenolol, and pindolol were used as reference compounds for AGP (+ in Figure 4), for which they exhibit a fractional binding of 98, 70, 58, and 30% respectively.37 The references facilitated the evaluation of the compounds with regard to their plasma protein binding levels. By use of the binding responses of pindolol and dipyridamole, the graph was divided into regions of low, intermediate, and high binding responses to both proteins. Paclitaxel and 10-deacetylbaccatin III showed low binding to at least one of the proteins. The new-generation

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Figure 5. Sensorgrams of interactions between atazanavir and HSA with overlaid theoretical curves for global analysis using a 1:1 interaction model. The residuals from the regression analysis are presented in the lower graph.

Figure 6. Sensorgrams of interactions between atazanavir (a) and docetaxel (b) and HSA. Theoretical curves obtained by nonlinear regression analysis using a model accounting for two nonequivalent binding sites are shown overlaying the experimental sensorgrams. The residuals from the regression analysis are presented below each set of sensorgrams.

taxanes all had increased binding to both AGP and HSA, with ortataxel and docetaxel having the highest binding levels of all taxanes to the two proteins. Saquinavir, ritonavir, and lopinavir were classified as having high binding to both HSA and AGP. All other test

compounds (i.e., excluding reference compounds) showed intermediate binding to both proteins. Additional information about the interaction between taxanes and HIV protease inhibitors with serum proteins was obtained by using a more elaborate experimental design.



Table 1. Kinetic Constants for the Interactions between Taxanes and HIV Protease Inhibitors and Human Serum Albumin compd

kon1 (mM-1 s-1)

koff1 (s-1)

KD1 (µM)

kon2 (M-1 s-1)

koff2 (s-1)

KD2 (µM)

amprenavir atazanavir saquinavir indinavir ritonavir lopinavir nelfinavir 7-hemisuccinyl paclitaxel IDN5390 docetaxel

22.1 9.6 25.0 2.5 7.3 25.9 1.8 7.3 5.9 7.7

2.750 1.430 0.856 1.230 0.466 0.619 0.413 1.183 1.875 1.505

124 149 34 495 64 24 230 192 319 199

208.0 41.8 282.0 21.2 347.0 10.1 12.6 36.4 33.1 43.7

0.068 30 0.025 00 0.085 00 0.011 90 0.065 80 0.000 06 0.000 07 0.033 73 0.045 25 0.069 10

328 599 301 559 189 6 6 1171 1825 2114

Figure 7. Interaction kinetic plot for HSA interactions based on interactions with two independent binding sites (data are taken from Table 1): docetaxel (green triangle), IDN5390 (green square), 7-hemisuccinylpaclitaxel (green circle), amprenavir (red triangle), atazanavir (red circle), indinavir (blue triangle), lopinavir (blue square), nelfinavir (blue diamond), ritonavir (blue circle), saquinavir (red square). The diagonal lines are isoaffinity lines representing the affinity KD ) koff/kon.

Figure 8. Sensorgrams of interaction between docetaxel and AGP and theoretical curves for a 1:1 binding model. Residuals from the nonlinear regression analysis are displayed in the lower graph.

2.2. HSA Interaction Kinetics. To monitor the kinetics of the interaction with HSA and AGP, compounds were injected over both of the plasma protein surfaces at different concentrations (usually up to 300 µM). Interactions of all compounds with HSA were complex and could not be described by a simple 1:1 interaction model, as exemplified in Figure 5. There are deviations between the experimental and the theoretical sensorgrams in both the association and dissociation

phases. Although association and dissociation appeared to be fast, there was a second, slower association phase preventing equilibrium from being reached rapidly. This indicated that interactions occurred with multiple nonequivalent or cooperative binding sites or that binding of ligand induced conformational changes. A model accounting for two nonequivalent binding sites (“heterogeneous ligand” according to Biacore terminology) could be used to describe the experimental sensorgrams

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for all HIV-1 protease inhibitors and three of the taxane family compounds (docetaxel, 7-hemisuccinylpaclitaxel, and the seco analogue). Typical sensorgrams for concentration series of atazanavir and docetaxel are shown overlaid with the theoretical curves from a global fitting using this model in parts a and b of Figure 6, respectively. The KD, kon, and koff values for two sites were thus determined for 10 compounds (Table 1). The sensorgrams for paclitaxel, ortataxel, biotinylpaclitaxel, 10-deacetylbaccatin III, or 14β-hydroxybaccatin III 1,14-carbonate could not be described by any simple interaction model tried because of high bulk effects, low responses, or lack of concentration dependence. These features were most likely related to problems in the sample preparation due to the extremely low aqueous solubility of taxanes. In addition, dissolved compounds have a tendency to aggregate, which can result in precipitation during experiments. Since HSA obviously has at least two independent binding sites, it was not meaningful to use the maximal response at saturation in a steady-state approach to get an estimate of an apparent KD for this interaction, a simple procedure that can be used to get apparent KD values for 1:1 interactions. The kinetic constants for the 10 compounds kinetically characterized were displayed in a kon vs koff graph (Figure 7). Two apparent sites with different kinetics could clearly be distinguished in the graph. Interestingly enough, these apparent sites could be depicted as “rapid” and “slow” with respect to association and dissociation rates. There is no significant difference between the affinities for these two sites. This is a new interpretation with respect to the traditional distinction of “high-” and “low-” affinity sites in HSA. Moreover, 2 of the 10 compounds analyzed with this model, lopinavir and nelfinavir, showed a “superslow” interaction; it is not clear yet if this represents an additional site or if it is a result of a particularly slow interaction with the “slow site”, or if it is an artifact from fitting a complex model to the data. 2.3. AGP Interaction Kinetics. The interactions between the studied compounds and AGP were also complex but different from those for HSA. The sensorgrams for the interaction between docetaxel and AGP using a 1:1 interaction model are shown in Figure 8. A variety of other models, accounting for multiple binding sites and conformational changes, were also used. However, none were satisfactory for kinetic analysis of the interactions. Since AGP also has several potential independent binding sites and may exhibit ligandinduced conformational changes, it was not meaningful to use a steady-state approach to get an estimate of an apparent KD for this interaction either. The basic method of using report points, and visualization as in Figure 4, was the most informative strategy available for analysis of AGP interactions.

tumors, preventing oral administration for its therapeutic use.38 The poor bioavailability of paclitaxel is partially due to elimination of the drug from the cell by P-glycoprotein (an efflux-pump abundant in the gastrointestinal tract).18 Ortataxel is a new-generation taxane, with higher bioavailability than paclitaxel; experiments in nude mice showed that when ortataxel is administered orally, it is rapidly absorbed (within 1 h) and its bioavailability is about 50%. It has been established that the higher bioavailability of this newgeneration taxoid is primarily due to the 1,14 carbonate moiety.39,40 Furthermore, the molecule is active against two paclitaxel-resistant cell lines in which the Pgp is overexpressed, and good antitumor activity was observed after oral or intravenous administration.19 Similarly, the seco derivative IDN5390, an oral taxane candidate for a protracted treatment schedule, has recently been discovered to be effective and welltolerated when administered orally.41 Published bioavailability data for HIV protease inhibitors was difficult to use for correlation analysis with the present data because there are large variations in the reported values probably due to differences in conditions and experimental designs. For example, the bioavailability reported for nelfinavir ranges from 20% to 80%. The hard-gelatin capsule of saquinavir has an erratic oral bioavailability of 4% or less, indinavir has a bioavailability that varies under different conditions, and ritonavir is known to have good bioavailability.20 This is probably a reflection of the complexity of bioavailability that is dependent not only on passive absorption and plasma protein binding but also on active transport and metabolism. 1. Drug-Lipid Membrane Interactions. The current method allowed the membrane interactions of taxanes and HIV protease inhibitors to be estimated from injections of a single concentration as low as 20 µM. The lipid membranes were prepared from liposomes that fuse and form lipid bilayers on the amphipilic L1 sensor chip used in these studies.42 The surface thus resembles a membrane to which a drug may be transiently associated with while passing through, although the exact mechanism for the interaction is not clear. Some of the compounds exhibited complex sensorgrams, indicating that interactions could not be simply described by standard interaction models. Instead, the response was determined after a defined interaction time and used to describe the interaction. Importantly, the choice of liposome mixtures influenced the ranking, a factor that should be evaluated and matched with in vivo bioavailability data for each drug discovery project the method is applied to. The data revealed that the new-generation taxanes had higher interaction responses for membranes compared to the parent drugs and that the recognition forces of taxane-membrane interactions are modulated by polarity. This is in accordance with publications stating that passive membrane transport involves both hydrophobic and electrostatic interactions.43,44 An exception was docetaxel, which had a high response despite its low bioavailability. This is most likely a result of its metabolic breakdown by the enzyme CYP3A4.45 The important role of hepatic metabolism in the disposition

Discussion Although paclitaxel is an efficient antineoplastic drug, there are many problems associated with its clinical application. As with many cytotoxic drugs, one of the major problems is a very low and/or a variable bioavailability;17 paclitaxel typically has a bioavailability of 10%. Moreover, when administered orally, it shows a complete lack of efficacy against hypersensitive human



of docetaxel is supported by the large amount of metabolites recovered from the bile.46 The lipid membrane assay could be applied for in vitro screening of the absorption levels of HIV-1 protease inhibitors, although we lacked the capability of performing a meaningful correlation analysis with in vivo data at this stage. Although there are marked differences in the intracellular accumulation of HIV protease inhibitors in vitro, with nelfinavir achieving intracellular concentrations of approximately 90-fold, saquinavir approximately 30-fold, ritonavir 3- to 7-fold, and indinavir demonstrating no appreciable intracellular accumulation,47 there are differences in the antiviral kinetics of these compounds that may be a result of differences in the rates of cellular clearance.48 Nevertheless, there was a clear correlation between structural features for HIV protease inhibitors and high responses for membranes (i.e., saquinavir and nelfinavir), indicating that functional groups that increase membrane absorption can be incorporated into inhibitor structures. Additional analysis is required to set up selection criteria for HIV protease inhibitor lead compounds. 2. Drug-Plasma Protein Interactions. There was a surprising correlation between the interactions with HSA and AGP. Although AGP typically binds basic and neutral lipophilic drugs and acidic drugs are generally assumed to be bound by HSA,12 this type of simplistic assumption is obviously not valid for taxanes and HIV protease inhibitors. However, the observed correlation is insignificant at this stage when little is known about the structural details of the interactions. It is of greater significance that both proteins can bind both types of compounds and that both proteins may be of importance for their distribution in vivo. HIV protease inhibitors, with the exception of indinavir, are more than 90% protein-bound, mainly to AGP. However, the issue of protein binding in antiviral therapy is complex and the variations are considerable.13 The data presented here support the relatively low protein binding of indinavir but show that HSA has a similar capacity to bind HIV protease inhibitors as AGP. Obviously the physiological concentrations of the two proteins will determine the distribution of the protein-bound inhibitors in vivo. 2.1. Human Serum Albumin. Human serum albumin has a highly flexible structure with multiple domains, providing a variety of sites to which a broad spectrum of compounds can interact.10,49 These sites are often defined as high-affinity sites with respect to certain types of compounds, although the classification is often ambiguous. The present study may help to resolve this ambiguity. Two independent sites could be defined, both for taxanes and for HIV protease inhibitors. But instead of differing in affinity (KD), they differed in their association and dissociation rate constants, the two parameters that define affinity, KD ) koff/kon. A “rapid site” was characterized by rapid association and dissociation rates (2-26 mM-1 s-1 and 0.4-3 s-1, respectively) and a “slow site” by slow association and dissociation rates (10-350 M-1 s-1 and 0.01-0.08 s-1, respectively). Note the difference in units for the association rate constants. This can be a result of two sites

that differ in their accessibility rather than in their capacity to be occupied by the ligand. However, the interactions with some of the studied taxanes were not well-described by this model, suggesting that additional complexities may occur. For example, it is likely that some interactions involve a ligandinduced conformational change. It is difficult to unambiguously distinguish such behavior from two independent sites based only on this type of kinetic experiments. Competition experiments or experiments using mutant protein could be useful to explore the issue further. However, for the compounds whose kinetics could be determined, the statistical tools in the data analysis favored the two-site model over the ligand-induced conformational change model. It was not meaningful to try to extend the models further. Circular dichroism displacement experiments indicate that HSA does not have a high-affinity binding site for taxanes and that the relatively low affinity of 7sptx to the protein is a result of interactions with several lowaffinity binding sites.35 This is supported by the present study, where 7-hemisuccinylpaclitaxel and the two other taxanes that gave reliable data (IDN5390 and docetaxel) interacted with the two apparent sites with affinities of 200-300 µM and 1-2 mM, respectively. They had similar association and dissociation kinetics for the two sites, confirming that taxanes appear to bind with similar characteristics and that the current structural modifications did not alter their interaction with HSA significantly. The slightly lower affinity for the seco taxane (IDN5390) compared to 7-hemisuccinylpaclitaxel and docetaxel is supported by preliminary biochromatographic experiments (data not shown). The kinetics of the interactions between HIV-1 protease inhibitors and HSA varied extensively. Nelfinavir and lopinavir showed the highest affinities for HSA, a result of a possible “superslow” interaction with one of the apparent kinetic sites. Alternatively, this may be attributed to a supplementary low-saturation binding site. Such outliers may also be the result of fitting a complex model to the data and should be verified by additional experiments and analysis. Also saquinavir and ritonavir show high affinity for HSA. This was supported by biochromatographic experiments, where saquinavir and ritonavir have a much longer retention time on an HSA column than atazanavir and amprenavir, confirming the present ranking of these compounds for HSA (data not shown). These results are in keeping with the significant reduction of the inhibitor activity observed in the presence of HSA.50 This effect is indeed relevant for those inhibitors showing higher affinity to the carrier. 2.2. r1-Acid Glycoprotein. There are discrepancies in the literature concerning the number of binding sites and the binding constants for drug interactions with AGP, a protein with many complex features.12 It is therefore not surprising that it was not possible to determine the kinetic constants for drug interactions with AGP in this study. More extensive experiments are required in order to determine the mechanism for interactions of drugs with AGP, where relevant kinetic parameters can be determined. The present study shows that the biosensor technique has the capacity for such mechanistic studies.

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3. Novel Strategies for in Vitro ADME Studies. The biosensor technique that we adopted has previously been used by others in pioneering studies of drug interactions with lipid membranes28-30 and human serum albumin.31-34,51 The present study confirms that the technique can be used as a tool in early in vitro ADME studies but also shows that it can provide more detailed information about the characteristics of interactions and the target proteins. For example, the drug binding sites on HSA were found to differ more in terms of accessibility and recognition rather than in affinity, and AGP was proved to display complex interaction kinetics with drugs. Another approach, based on isothermal titration calorimetry, has been introduced for studies of the serum protein binding of HIV-1 protease inhibitors.50 The affinities for AGP and HSA ranked similarly to those found in this study with the exception of saquinavir. The study did not reveal complex interaction kinetics, preventing further comparisons between the two studies. The present study combines the possibility of screening compound libraries and quick analysis methods with a more rigorous characterization of interactions when necessary. The difference lies in the experimental design and data analysis. It consequently allows a simple twostep procedure for in vitro ADME studies, and the required information dictates the type of experiment that needs to be performed. This makes the strategy highly efficient.

LTD, Tokyo, Japan) was coupled to the instrument. The running buffer for the plasma protein interaction studies was 0.01 M phosphate buffer, 0.0027 M potassium chloride, 0.137 M sodium chloride, and 3% DMSO. For the lipid membrane assay the running buffer was 0.05 M phosphate buffer, 0.0027 M potassium chloride, 0.97 M sodium chloride, and 3% DMSO. 1. Liposome Preparation and Capture. Liposomes were prepared essentially as described previously.52 Two types of liposomes were prepared, one consisting of a single type of lipid, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), and one consisting of a synthetic phospholipid mixture (DOPE/ DOPS/DOPC) with 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 1,2-dioleoyl-sn-glycero-3-(phospho-L-serine) (DOPS), and 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) in a 5:3:2 (w/w) ratio (Avanti Polar Lipids, AL). DOPE/DOPS/DOPC and POPC mixtures (0.5 mM) were captured on an L1 sensor chip (Biacore, Uppsala, Sweden) using a flow rate of 10 µL/min and a contact time of 180 s. They were captured on two different spots on the same sensor chip, allowing injection of compounds to both lipid membrane types simultaneously. The DOPE/DOPS/DOPC liposomes were captured on the first spot on the chip in order to permit a longer stabilization period for this surface. This reduced problems because of a slight baseline drift for this lipid surface. Capture levels of 5300 and 7500 RU were consistently achieved for DOPE/DOPS/DOPC and POPC liposomes, respectively. Each chip was conditioned with 40:60 (v/v) 2-propanol/NaOH, and the same solution was used for washing the flow system between sample injections. A detection spot without captured liposomes was used as a reference. 2. Lipid Membrane Interaction Assay. Fresh liposomes were captured before each compound injection. Samples were injected at 20, 100, and 150 µM for 60 s, and dissociation was followed for 30 s using a flow rate of 30 µL/min. All experiments were performed at 37 °C and in duplicate. Regeneration was performed with two 12 s injections of 40:60 (v/v) 2-propanol/NaOH to strip the lipids completely from the surfaces. The chip was allowed to stabilize for 30 s before new liposomes were captured. A 1-min blank (running buffer alone) was injected to check for carryover from the previous cycle. Solvent refractive index (RI) correction (see below) was run every 20th sample cycle. Four drugs with low, intermediate, and high lipid membrane affinities were used as references: ceftriaxone, famotidine, hydrochlorothiazide, and propranolol. They were injected every 36th cycle at 20, 100, and 150 µM. Absorption data were taken from a report point occurring after injection of the compound for 60 s. 3. Plasma Protein Preparation and Immobilization. HSA and AGP were immobilized on two different detection spots on the same series S CM5 chip (Biacore AB, Uppsala, Sweden). Experiments could thus be performed with injection of compounds to both proteins simultaneously. A detection spot with the unmodified dextrane matrix was used as reference. 3.1. Human Serum Albumin. HSA, essentially fatty acid and globulin free, was from Sigma-Aldrich Sweden AB, Stockholm, Sweden. The protein was used without further purification. A stock solution was prepared in phosphate buffered saline (pH 7.4) and stored at -20 °C. Immediately prior to use, this solution was diluted to a concentration of 280 µg/mL in 0.010 M sodium acetate, pH 5.0. HSA was immobilized to the sensor chip by amine coupling at 25 °C, essentially according to standard Biacore procedures. The carboxymethyl-modified dextrane polymer of the CM5 chip was activated with 0.2 M 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC) and 0.05 M N-hydroxysuccinimide (NHS) in a 1:1 mixture for 10 min (EDC and NHS were from a Biacore AB amine coupling kit, Biacore AB, Uppsala, Sweden). HSA was injected for a time set to give a final immobilization level between 9000 and 10 000 RU. This was followed by a 7-min injection of 1 M ethanolamine, pH 8.5, blocking remaining unreacted groups on the surface. 3.2. r1-Acid Glyccoprotein. AGP (99% purity) was from Sigma-Aldrich Sweden AB, Stockholm, Sweden. The protein was modified before immobilization in order to allow coupling

Conclusions The current study shows the application of a biosensor for screening and kinetic characterization of interactions between drugs and lipid membranes as well as serum proteins, the method being applicable for the efficient investigation of series of different ligands. This work has involved studies of compounds of relevance for cancer and AIDS treatment. The current strategy was useful for the detection of structural features of importance for ADME. The lipid membrane interaction assay was set up at a drug concentration as low as 20 µM (i.e., significantly lower than those used in previous investigations), thus also allowing the reliable study of particularly insoluble compounds. Although the in vivo efficacy of drugs is obviously a very complex process, the possibility of determining membrane absorption (linked to percentage fraction absorbed) and plasma protein binding (correlated with percentage drug bound and the active drug concentration) provides two important pieces to the puzzle. In addition to providing important information on the absorption and on the distribution of this type of drug, this strategy can facilitate the elucidation of the binding characteristics of both HSA and AGP, two proteins with complex binding characteristics. Indeed, the kinetic constants for interactions with HSA have been determined for some of the analyzed drugs, allowing discrimination between the binding sites based on the rates of the recognition process rather on the affinities. Materials and Methods All measurements were performed with a Biacore S51 optical biosensor from Biacore AB, Uppsala, Sweden. An online degassing system (Degasys Ultimate DU4010, Uniflows Co.,



via thiol groups. PDEA-AGP was synthesized by dissolving 1 mg of AGP and 3 mg of 2-(2-pyridinyldithio)ethanolamine hydrochloride (PDEA) in 1.0 mL of 0.1 M 2-(4-morpholino)ethanesulfonic buffer (MES), pH 5.0. The solution was incubated for 10 min at room temperature after addition of 50 µL of 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC). The modified protein was separated from PDEA by applying it to a NAP 10 column (Amersham Biosciences, Uppsala, Sweden) and eluting with 1.25 mL of protein with 0.01 M citrate buffer, pH 3.6, resulting also in an appropriate buffer exchange. The modification reaction was believed to be quantitative, resulting in approximately 800 µg/mL final concentration of the modified protein. Aliquots of PDEA-AGP were stored at -20 °C and diluted to 270 µg/mL in 0.01 M citrate buffer, pH 3.6, immediately before immobilization. Immobilization of the PDEA-modified AGP was performed at 25 °C. The carboxymethyl-modified dextrane polymer of the CM5 sensor chip was activated with a 2-min injection of a 1:1 mixture of 0.2 M 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC) and 0.05 M N-hydroxysuccinimide (NHS). Disulfide bridges were introduced on the sensor surface by injection of 40 mM cystamine dihydrochloride in 0.1 M sodium borate, pH 8.5, for 3 min. Disulfides were reduced to free thiol groups by injection of 0.1 M dithioerythrithol (DTE) in 0.1 M sodium borate, pH 8.5, for 3 min. PDEA-modified AGP was then injected for 26 min. Excess reactive groups were deactivated by a 4-min injection of 20 mM 2-(2-pyridinyldithio)ethanolamine and 1 M NaCl in 0.1 sodium acetate, pH 4.3 (PDEA-NaCl). The immobilization level achieved varied from 8200 to 10 800 RU. 4. Plasma Protein Interaction Studies. Screening. An initial evaluation of the binding of test compounds to plasma proteins was performed at a single fixed concentration (30 µM) of all compounds and a flow rate of 30 µL/min. Reference compounds with high or low binding to HSA or AGP were also tested under the same conditions. Compounds were injected for 60 s, and dissociation was followed for 30 s. Regeneration was not required between injection cycles. New surfaces were stabilized by a series of blank injections. A buffer injection between every sample cycle was included in order to check for carryover. Solvent RI correction (see below) was run every 20th sample cycle. All samples were injected in duplicate, and experiments were repeated three times using different immobilizations of each protein. 4.1. Determination of Kinetic Parameters. Kinetic parameters for the interactions were determined by randomly injecting a series of different concentrations of each sample. Concentrations of up to 300 µM were typically used; the maximal concentration was defined by the solubility of the compounds under the current experimental conditions. Interactions were studied with a flow rate of 30 µL/min and 60 s association and 30 s dissociation times. Regeneration was not required between injection cycles. Carryover control injections were included in the assay (as above). Solvent RI correction (see below) was run every 20th sample cycle. 5. Solvent RI Correction. Because of the high refractive index of DMSO, used as solvent for the tested compounds, signals may result from small variations in DMSO concentration between sample solutions and running buffer. These nonspecific signals can be significant for interaction studies with low molecular weight analytes, since the specific signals are proportional to the molecular weight of the analyte. Therefore, a solvent refractive index (RI) correction procedure was used. It was based on a calibration curve obtained by injecting a series of eight DMSO concentrations ranging from 2.5 to 3.8% in all three spots, resulting in signals between -500 and +1500 relative to the baseline. Samples with nonspecific signals outside the calibration range were not included in the analysis. 6. Data Evaluation. All data were corrected for nonspecific signals resulting from interaction with the reference spot (“reference subtraction”) and from differences in refractive index (“solvent RI correction”). The ranking of compounds interacting with plasma proteins and lipid membranes was

performed after normalizing the data with respect to the molecular weight of each compound. Data were further normalized against positive controls (i.e., the response was set to 100 for rac-naproxen in the HSA assay, for dipyridamole in the AGP assay, and for propranolol in the lipid membrane assay). Evaluation software version 1.2 for Biacore S51 and BIAevaluation 4.0.1 were used for data analysis. 7. Taxanes, HIV-1 Protease Inhibitors, and Reference Compounds. Taxanes docetaxel, paclitaxel, 10-deacetylbaccatin III, 14β-hydroxybaccatin III 1,14-carbonate, IDN5390, and ortataxel were kindly provided by Indena S.p.A., Italy. 7-Hemisuccinylpaclitaxel and 7-biotinylpaclitaxel were synthesized by Andrea Guerrini, CNR, Bologna, Italy. HIV protease inhibitors ritonavir, nelfinavir, saquinavir, atazanavir, amprenavir, lopinavir, and indinavir were a gift from Medivir AB, Sweden. Reference compounds rac-warfarin, racnaproxen, diazepam, digitoxin, quinine, dipyridamole, alprenolol, propranolol, pindolol, ceftriaxone, famotidine, and hydrochlorothiazide were from Sigma-Aldrich Sweden AB, Stockholm, Sweden. All samples were dissolved at 0.01 M concentration in 100% DMSO and stored at -20 °C. Dilutions of the compounds were made in appropriate buffers just before use. All components in the buffers (e.g., salts and DMSO) were carefully matched with the running buffer in order to avoid nonspecific signals due to differences in the refractive indexes of samples and buffers.

Acknowledgment. The authors gratefully acknowledge Biacore AB, Uppsala, Sweden, for the use of the Biacore S51 instrument and for kindly providing laboratory materials and helpful advice. We are also indebted to Medivir AB, Huddinge, Sweden, and Indena S.p.A., Milan, Italy, for financially supporting this project and for supplying most of the samples used in this study. Dr. Samanta Cimitan is especially thankful to Indena S.p.A., Milan, Italy, for the precious funds made available for her position both in Italy and in Sweden. Appendix Abbreviations. RI, refractive index; SPR, surface plasmon resonance; ADME, absorption, distribution, metabolism, and excretion; HIV, human immunodeficiency virus; HSA, human serum albumin; AGP, R1-acid glycoprotein; KD, equilibrium dissociation constant; koff, dissociation rate constant; kon, association rate constant; DOPE, 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine; DOPS, 1,2-dioleoyl-sn-glycero-3-phospho-L-serine; DOPC, 1,2-dioleoyl-sn-glycero-3-phosphocholine; POPC, 1-palmitoyl-2-oleoyl-sn-glycero-phosphocholine; EDC, 1-ethyl3-(3-dimethylaminopropyl)carbodiimide hydrochloride; NHS, N-hydroxysuccinimide; PDEA, 2-(2-pyridinyldithio)ethanolamine hydrochloride; MES, 2-(4-morpholino)ethanesulfonic buffer; DTE, dithioerythrithol. References (1) Bleicher, K. H.; Bo¨hm, H. J.; Mu¨ller, K.; Alanine, A. I. Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Discovery 2003, 2, 369-378. (2) Buchwald, P.; Bodor, N. Octanol-water partition: searching for predictive models. Curr. Med. Chem. 1998, 5, 353-380. (3) Stouch, T. R.; Kenyon, J. R.; Johnson, S. R.; Chen, X. Q.; Doweyko, A.; Li, Y. In silico ADME/Tox: why models fail. J. Comput.-Aided Mol. Des. 2003, 17, 83-92. (4) Hodgson, J. ADMETsturning chemicals into drugs. Nat. Biotechnol. 2001, 19, 722-726. (5) van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discovery 2003, 2, 192-204. (6) van De Waterbeemd, H.; Smith, D. A.; Beaumont, K.; Walker, D. K. Property-based design: optimization of drug absorption and pharmacokinetics. J. Med. Chem. 2001, 44, 1313-1333.

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