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METABOLISM, TRANSPORT, AND PHARMACOGENOMICS

Impact of Impurity on Kinetic Estimates from Transport and Inhibition Studies

Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland (P.G., J.E.P.); and Departamento de Farmacia, Facultad de Química, Pontificia Universidad Católica de Chile, Santiago, Chile (P.G.)

	Abstract

Top Abstract Materials and Methods Results Discussion Appendix A Appendix B References

 
Although in vitro transport/inhibition studies are commonly performed on impure drug candidates to screen for pharmacokinetic properties in early development, quantitative guidelines concerning acceptable impurity levels are lacking. The broad goal was to derive models for the effect of impurity on transport and inhibition studies and identify the maximum allowable impurity level that does not bias assay results. Models were derived, and simulations were performed to assess the impact of impurity on substrate properties K_t and J_max and inhibition K_i. Simulation results were experimentally challenged with a known amount of impurity, using the intestinal bile acid transporter as a model system. For substrate uptake studies, glycocholate served as substrate and was contaminated with either a very strong, strong, or moderate impurity (i.e., taurolithocholate, chenodeoxycholate, or ursodeoxycholate, respectively). For inhibition studies, taurocholate and glycocholate together was the substrate/inhibitor pair, where glycocholate was contaminated with taurolithocholate. There was high agreement between simulation results and experimental observations. It is not surprising that, in the inhibition assay, potent impurity caused test compound to appear more potent than the true potency of the test compound (i.e., reduced inhibitory K_i). However, results in the transport scenario surprisingly indicated that potent impurity did not diminish test compound potency but, rather, increased substrate potency (i.e., reduced Michaelis-Menten substrate K_t). In general, less than 2.5% impurity is a reasonable target, provided the impurity is less than 10-fold more potent than test compound. Study results indicate that careful consideration of possible impurity effect is needed when quantitative structure-activity relationship analysis cannot explain high compound potency from transport or inhibition studies.

However, test compounds in early development often contain chemical impurities, including intermediates that bear structural similarity to the target test compound. The presence of such impurities has potential to affect the results of pharmacologic assays, including ADME screening results. Guidelines on compound purity are not provided in the Journal of Pharmacology and Experimental Therapeutics. Since January 2007, the Journal of Medicinal Chemistry now requires that key target compounds possess a purity of 98% or more. However, well developed guidelines and their rationale regarding an acceptable level of impurity, based on possible impurity impact on assay results during early development, are surprisingly lacking. Guidance on impurity effects on ADME screening studies would be helpful.

The present study concerns two types of ADME transport studies: inhibition studies and transport/uptake studies. It is presumed in a competitive binding study (e.g., inhibition study) that impurity with a potency greater than that of test compound may cause the test compound to appear more potent than it is in actuality. This expectation was found to be correct here; quantitative guidelines are provided. It is surprising that an expectation in which a potent impurity would diminish the apparent potency of a test compound in the uptake assay (i.e., increase Michaelis-Menten K_t) was found here to be incorrect. Rather, potent impurity, which reduces test compound flux, resulted in test compound to appear to possess higher substrate affinity (i.e., exhibit a lower K_t). This study provides quantitative guidelines, which are currently lacking, on maximal impurity levels to avoid bias on transporter parameter estimates (i.e., K_t, J_max, and K_i) in early drug discovery. Results have implications for other types of early discover assays, such as pharmacologic binding studies.

	Materials and Methods

Top Abstract Materials and Methods Results Discussion Appendix A Appendix B References

 
Overall Study Design. Both simulation studies and experimental studies were performed for both transport/uptake studies, as well as inhibition studies. Table 1 summarizes the four types of studies. In transport/uptake studies, the impurity is a contaminant of the substrate. Simulation studies were conducted over a wide range of conditions. The experimental uptake studies employed taurolithocholic acid (TLCA), chenodeoxycholic acid (CDCA), and ursodeoxycholic acid (UDCA) as very strong, strong, and moderate potent impurities, respectively. In inhibition studies, the impurity is a contaminant of the inhibitor.

Materials. [³H]Taurocholic acid (TCA; 10 µCi/mmol) and [¹⁴C]-glycocholic acid (GCA; 55 mCi/mmol) were purchased from American Radiolabeled Chemicals, Inc. (St. Louis, MO). TCA, GCA, TLCA, and UDCA were from Sigma-Aldrich (St. Louis, MO). CDCA was obtained from TCI America (Portland, OR). Geneticin (G-418), fetal bovine serum, trypsin, and Dulbecco's modified Eagle's medium were purchased from Invitrogen (Carlsbad, CA). All other reagents and chemicals were of the highest purity commercially available.

Cell Culture. Stably transfected hASBT-MDCK cells were cultured as described previously (Balakrishnan et al., 2005). In brief, cells were grown at 37°C, 90% relative humidity, 5% CO₂ atmosphere and fed every 2 days. Culture media consisted of Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 50 units/ml penicillin, and 50 µg/ml streptomycin. G-418 was added at 1 mg/ml to maintain selection pressure. Cells were passaged after 4 days or after reaching 90% confluence.

Uptake Studies. Uptake studies were performed to obtain kinetic parameters that relate to compound binding and subsequent translocation into the cell monolayer. Stably transfected hASBT-MDCK cells were grown on 12-well plates (3.8 cm²; Corning Inc., Corning, NY) and grown under conditions described above. In brief, cells were seeded at a density of 1.5 million/well and induced with 10 mM sodium butyrate at 12 to 15 h at 37°C before study on day 4. Cells were washed three times with Hanks' balanced salt solution (HBSS) or modified HBSS before uptake assay. Studies were conducted at 37°C, 50 rpm for 10 min in an orbital shaker. Uptake buffer consisted of either HBSS, which contains 137 mM NaCl, or a sodium-free, modified HBSS where NaCl was replaced by 137 mM tetraethylammonium chloride, pH 6.8. Because ASBT is sodium-dependent, studies using sodium-free buffer allowed for the measurement of passive uptake. Kinetics of hASBT-mediated GCA uptake (n = 3) was assessed at different donor concentrations (1–200 µM spiked with 0.2 µCi/ml [¹⁴C]GCA) in the presence and absence of impurity. When impurity was present, each GCA donor solution was contaminated with TLCA, CDCA, or UDCA (i.e., impurity) to yield a mole fraction of impurity, X_i, of 2, 4, 6, 8, and 10%, respectively. K_i of these impurities was obtained from GCA-uptake inhibition studies (see below).

At the end of the assay, active uptake was stopped by washing the cells three times with chilled sodium-free buffer. Cells were then lysed with 0.25 ml of 1 N NaOH overnight, allowing for complete evaporation, and reconstituted with 0.50 ml of 0.5 N HCl. Cell lysate was counted for associated radioactivity using an LS6500 liquid scintillation counter (Beckman Instruments, Inc., Fullerton, CA).

Inhibition Assay. To characterize hASBT binding affinities, cis-inhibition studies of TCA or GCA uptake were conducted as described. Cells were exposed to donor solutions containing relevant substrate (2.5 µM TCA + 0.5 µCi/ml [³H]TCA or 5 µM GCA + 0.2 µCi/ml [¹⁴C]GCA) and inhibitor (1–200 µM) for 10 min. GCA inhibition of TCA was measured in the absence and presence of impurity. When impurity was present, each GCA donor solution was contaminated with TLCA (i.e., impurity) to yield a mole fraction of impurity, X_j, of 2, 4, 6, 8, and 10%, respectively. TLCA inhibition of TCA uptake was also measured to obtain K_j (see below). After 10 min, donor solution was removed; cells were washed three times with chilled sodium-free buffer, lysed, and counted for associated radioactivity (i.e., TCA). Inhibition data were analyzed in terms of inhibition constant K_i as described below.

Simulation of Substrate Transport: Impurity Effect on Substrate Flux. To assess the impact of impurity on substrate flux, simulations were performed using eqs. 1 and 2 (see Appendix A) for scenarios with and without impurity, respectively. Equations 1 and 2 denote the impurity-present model and the impurity-absent model, respectively, for transport/uptake studies.

(1)

(2)

Flux in the presence of impurity (J_Xi) was calculated using eq. 1 over a range of K_t, X_i, and K_i values. Substrate K_t was 5, 50, and 500 µM; substrate concentration (S) was one-tenth of K_t. Impurity level (X_i) was varied from 0 to 10% mole fraction, with greater sampling for larger K_t scenarios. K_i was 0.05, 0.5, 5, 50, and 500 µM, respectively. J_max, P_ABL, and P_p were fixed to 0.5 pmol/cm²/s, 70 x 10^–6 cm/s, and 0.5 x 10^–6 cm/s, respectively, in all cases (Balakrishnan et al., 2007). Equation 2 (i.e., flux without impurity, J) is eq. 1 when X_i = 0. The ratio J_Xi/J was calculated as a metric for impurity effect on flux and plotted against X_i. It is important to note that S was the actual assigned substrate concentration in donor; the concentration of substrate and impurity (when X_i > 0) was greater than S.

Simulation of Substrate Transport: Impurity Effect on K_t and J_max Estimates. To assess impurity effect on K_t and J_max, estimates from transport studies, simulated flux data were generated from impurity-present model (eq. 1). Across the simulations, K_t was 5, 50, or 500 µM, whereas J_max was 0.5 pmol/cm²/s. Because the aim of these simulation studies is to measure impurity effect on bias on estimated K_t and J_max parameter fits, these K_t values (i.e., 5, 50, and 500 µM) and J_max value (i.e., 0.5 pmol/cm²/s) are denoted as "true K_t" and "true J_max", respectively. In simulations, S was 1/20, 1/10, 1/5, 1/2, 1, 2, 5, 10, and 20 x K_t to assure saturation of active transport. P_ABL and P_p were 70 x 10 ^6– and 0.5 x 10^–6 cm/s, respectively. Simulated flux data were subsequently fitted to impurity-absent model (eq. 2) for each unique condition (i.e., unique K_t, K_i, and X_i scenario). Nonlinear regression was used to simultaneously estimate K_t and J_max using SigmaPlot 8.0 (SPSS Inc, Chicago, IL). In all cases, r² = 1.000. Results are discussed in terms of resulting bias in K_t and J_max, due to impurity, relative to "true K_t" and "true J_max" values that were employed in simulating flux data. K_t estimates were plotted against impurity level for each "true K_i" level. Similar plots were graphed for J_max estimates. Estimation error in K_t (or J_max) that exceeded 20% was considered unacceptably biased.

Simulation of Inhibition Studies: Impurity Effect on K_i Estimate. To simulate impurity influence on K_i estimate, eq. 3 (see Appendix B) was used to simulate inhibition profiles, where inhibitor was contaminated with impurity. Equation 3 is the impurity-present inhibition model.

(3)

It should be noted that, in contrast to transport simulations above where K_i is the inhibition constant of the impurity that contaminates the substrate, K_i is the unbiased inhibition constant of the inhibitor (i.e., the value to be measured in the inhibition study). K_j is the unbiased inhibition constant of the impurity that contaminates the inhibitor. Impurity is present in inhibitor at level X_j. Across simulations, K_t, J_max, P_ABL, and P_p were fixed to be 5 µM, 0.5 pmol/cm²/s, 70 x 10^–6 cm/s, and 0.5 x 10^–6 cm/s, respectively, which reflects active TCA transport across hASBT-MDCK monolayers (Balakrishnan et al., 2007). K_i was 0.05, 0.5, 5, 50, and 500 µM. Inhibitor concentration was 1/20, 1/10, 1/5, 1/2, 1, 2, 5, 10, and 20 x K_i. Impurity K_j was 0.5, 5, and 50 µM. An entire inhibition profile was generated for each level of impurity (X_j), which ranged from 0 to 10% mole fraction. Each inhibition profile (i.e., unique K_i, K_j, and X_j scenario) was fitted to impurity-absent inhibition model (eq. 4; see Appendix B) using nonlinear regression. Only K_i was estimated, whereas all other parameters assumed their true values. In all cases, r² = 1.000, with the exception for one extreme inhibition study simulation (K_i = 500 µM) where r² < 0.6.

(4)

Results are discussed in terms of resulting bias in K_i, due to impurity, relative to true K_i values that were employed in simulating inhibition profile. K_i estimates were plotted against impurity level for each true K_j level. Estimation error in K_i that exceeded 20% was considered unacceptably bias.

Impurity Effect on Active Uptake Kinetic Estimates: Experimental Evidence. A series of uptake experiments where model substrate GCA was contaminated with model impurity TLCA, CDCA, or UDCA were conducted to confirm simulation predictions. These bile acids were selected for several reasons. Previous data from our laboratory have shown GCA K_t to be 11.0 ± 1.9 µM. K_i values for TLCA, CDCA, and UDCA were 0.50 ± 0.05, 1.94 ± 0.17, and 22.6 ± 3.0 µM, respectively (Balakrishnan et al., 2006b). TLCA, CDCA, and UDCA were chosen as impurities because of their high structural similarity to the substrate probe (i.e., GCA) and because they represent cases that were K_t/K_i 100, 10, and 1, respectively.

The uptake format was chosen to keep P_p at a minimum since high passive permeability (i.e., low monolayer integrity on transport format) was found to hinder proper evaluation of impurity impact on active transport parameter estimates. For uptake studies, P_ABL was set to 1.5 x 10^–4 cm/s in analyzing experimental data (Balakrishnan et al., 2007). Given that the impurity-present uptake and inhibition models are based on the assumption of competitive inhibition between the compound of interest and the impurity, a Dixon's analysis was performed to investigate the inhibition mechanism of TLCA on GCA uptake.

Analysis of Experimental Data from Uptake Studies. Experimental data from GCA uptake studies (X_i = 0–10%, with and without sodium) and from impurity-mediated inhibition of GCA uptake were combined and fitted simultaneously to eqs. 1, 2, 4, and 5 in WinNonlin 5.2 (Pharsight, Mountain View, CA) using nonlinear regression to obtain "unbiased" estimates of GCA K_t, J_max, K_i, and P_p. Subsequently, values of these unbiased estimates were applied to eq. 1 (i.e., impurity-present uptake model) to simulate GCA-uptake profiles when X_i was 2, 4, 6, 8, and 10%, respectively. Predicted "biased" K_t and J_max were obtained by nonlinear fitting of these subsequent curves to eq. 2.

(5)

To challenge the predictive accuracy of the impurity-present model, GCA uptake in presence of impurity was fitted to eq. 2 (i.e., impurity-absent uptake model) to obtain observed biased GCA kinetic estimates, K_t and J_max, for each level of impurity mole fraction, X_i. Predicted bias and observed bias were compared.

Analysis of Experimental Data from Inhibition Studies. Compared to data analysis of uptake studies, the same approach was taken to challenge the impurity-present inhibition model and analyze impurity effect on GCA K_i. GCA inhibition of TCA uptake (with and without TLCA as impurity) and TLCA inhibition profile were fitted simultaneously to eqs. 3 and 4, respectively, using nonlinear regression to obtain unbiased estimates of K_i, K_j, J_max, and P_p. TCA K_t was obtained from parallel uptake studies. Subsequently, values of these unbiased estimates were applied to eq. 3 (i.e., impurity-present inhibition model) to simulate GCA inhibition profiles when X_j was 2, 4, 6, 8, and 10%, respectively. Predicted biased K_i was obtained by nonlinear fitting of these curves to eq. 4.

To challenge the predictive accuracy of the impurity-present inhibition model, GCA inhibition studies in presence of impurity were fitted to eq. 4 (impurity-absent inhibition model) to obtain observed biased GCA K_i for each level of impurity mole fraction, X_j. Predicted bias and observed bias were compared.

	Results

Top Abstract Materials and Methods Results Discussion Appendix A Appendix B References

 
Simulation results are presented first, followed by supporting experimental observations (Table 1).

Simulation of Substrate Transport: Impurity Effect on Substrate Flux. Simulations indicate that impurity generally decreased substrate flux across cell monolayers. For example, Fig. 1 illustrates the decrease in flux of a moderate substrate (i.e., K_t = 50 µM) in the presence of increasing amount of impurity, particularly for the more potent impurities. A 20% decrease in flux was observed when the impurity was a strong inhibitor (i.e., K_i = 0.5 µM; open circles) and present at approximately 3.5% molar fraction level. Meanwhile, only a 0.5% mole fraction of a most strong inhibitor (i.e., K_i = 0.05 µM; closed circles) was needed to cause a 20% decrease in flux. Simulations covering a broader range of K_t values showed similar trends, including the susceptibility of weaker substrates to more pronounced impurity effects (see Supplemental Fig. a).

View larger version (18K): [in this window] [in a new window]   Fig. 1. Decrease in total flux of substrate across a monolayer due to presence of impurity. Total flux of a substrate (J) was simulated using eq. 2 (i.e., impurity-absent model) for Kt level of 50 µM. Equation 1 (i.e., impurity-present model) modeled the influence of impurity on substrate total flux (JXi). When the impurity present, Xi, varied from 0 to 10% over five different levels of Ki (0.05, 0.5, 5, 50, and 500 µM, respectively), the impurity effect was assessed by the ratio of fluxes in the presence and absence of impurity (i.e., ratio of JXi versus J). The ratio JXi/J was 1 when no impurity was present and then decreased consistently as impurity level increased. The drop in total flux was more dramatic as the potency of the inhibitor increased.

View larger version (20K): [in this window] [in a new window]   Fig. 2. Flux profile of a substrate with Kt of 5 µM in the presence of 2% mole fraction impurity. Simulations were performed where Ki values of impurity varied from 0.05 to 500 µM. The total flux of substrate decreased with impurity potency. Apparent Jmax and apparent Kt both also decreased with impurity potency. For example, when the impurity Ki was 5 µM, apparent Jmax and apparent Kt were 0.48 pmol/cm /s and 4.8 µM, respectively. However, when Ki was 0.05 µM, apparent Jmax and apparent Kt were both 2.5-fold lower than these values.

Simulation of Substrate Transport: Impurity Effect on K_t and J_max Estimates. The impact of impurity on K_t and J_max was assessed by fitting simulated data from eq. 1 (i.e., impurity-present model) onto eq. 2 (i.e., impurity-absent model). This approach mimics the common scenario in early discovery where impurity is present, but data analysis assumes no impurity. Bias in kinetic estimates was negative to reduce the estimated values of K_t and J_max. Whereas this effect in J_max is intuitive, this effect on K_t would appear to be unexpected. As described above, impurity reduced substrate flux but resulted in the substrate to appear as a substrate with greater affinity than it truly possesses (i.e., estimated K_t < true K_t).

Figure 3 illustrates estimated K_t as a function of impurity level for a strong substrate (5 µM). Bias was deemed to occur when impurity caused more than a 20% error in estimate. From Fig. 3, the impurity potency needed to be at least 10-fold higher than that of the substrate to cause bias. When K_t/K_i = 10, the impurity level needed to cause bias was always approximately 2.5% mole fraction. For example, X_i = 2.5% lead the estimated K_t to be 4 µM when K_t/K_i = 10. Simulations covering a broader range of K_t values showed similar trends, including the susceptibility of weaker substrates to impurity effects (see Supplemental Fig. b)

View larger version (19K): [in this window] [in a new window]   Fig. 3. Impact of impurity on Kt estimates from transport studies when impurity not considered. Kt value was 5 µM. The mole fraction of impurity, Xi, varied from 0 to 10%. Ki values were 0.05 (filled circle), 0.5 (open circle), 5 (filled triangle), 50 (open triangle), or 500 µM (filled diamond). Impurity generally reduced estimated Kt. An estimated Kt of 80% or less than the true Kt value was considered unacceptably biased. Impurity level and inhibitory potency each promoted bias. Bias in Kt estimates was increasingly sensitive to impurity level when true Kt was at least 10-fold larger than Ki and was very large when Kt was weak while Ki was strong. It is interesting that analogous plots for bias in Jmax followed identical trends (see supplemental data).

Regarding effect of impurity on J_max, plots of estimated J_max versus X_i were identical to those in Fig. 3. It is interesting that X_i levels had the same relative effect on J_max as on K_t (Supplemental Fig. c). Although presented for impurity effect on K_t bias, Table 2 equally applies for J_max bias. For example, when K_t/K_i = 1 or less, bias in J_max did not manifest.

Whereas the effect on J_max is intuitive, this effect of impurity to reduce K_t would appear to be unexpected. K_t is often interpreted as an affinity parameter, such that impurity effects would result in the substrate to appear as a substrate with greater affinity than it truly possesses (i.e., estimated K_t < true K_t). Figure 2 illustrates the basis for this effect. Figure 2 was generated for X_i = 2% for a strong substrate (K_t = 5 µM). For the scenario K_i = 0.5 µM (open circles), estimated J_max and K_t were 0.415 pmol/cm²/s and 4.15 µM, respectively, which are each 17% less than the true values of 0.5 pmol/cm²/s and 5 µM, respectively. In this scenario, it can be interpreted that impurity effect to reduce J_max estimate causes a proportional effect on K_t estimate, vis-à-vis the J_max effect. Given that K_t is the concentration at half-J_max and because impurity reduces apparent J_max, impurity reduces apparent K_t.

Simulation of Inhibition Studies: Impurity Effect on K_i Estimates. In addition to transport studies, inhibition studies are frequently performed in development and design to evaluate the ability of a compound to inhibit the transport of a known substrate. Data are often interpreted as an inhibition constant (K_i). Here, impurity J_imp contaminates inhibitor I and competes against I (and substrate) for the same transporter in a noncooperative fashion (see Supplemental Fig. d). By virtue of impurity contributing additional inhibition, impurity can produce negative bias in K_i estimates from inhibition studies.

The extent of this bias is illustrated in Fig. 4 (substrate K_t = 5 µM) for a strong inhibitor (K_i = 5 µM) and shows bias depended on the affinity of the impurity (i.e., K_j) and its mole fraction, X_j. For example, a very strong impurity (K_j = 0.5 µM) at X_i = 2.5% caused 20% bias in estimated K_i, resulting in apparent K_i to be 4 µM. Simulations covering a broader range of K_i values showed similar trends, including the susceptibility of weaker inhibitors to more pronounced impurity effects (see Supplemental Fig. e). The impact of impurities on inhibition results mimics the above effect on transport studies.

View larger version (16K): [in this window] [in a new window]   Fig. 4. Impact of impurity on Ki estimate from inhibition studies when inhibitor contaminated with impurity. Ki is inhibition constant of inhibitor. Kj is inhibition constant of impurity. Impurity contaminated the inhibitor. True Ki value was 5 µM. Impurity level was varied from 0 to 10% mole fraction. Kj was 0.5 (filled circle), 5 (open circle), and 50 µM (filled triangle), respectively. Impurity generally resulted in either no effect or decreased apparent Ki. An estimated Ki of 80% or less than the true Ki was considered unacceptably biased. When present, bias was always negative (i.e., estimated Ki less than true Ki). No bias in Ki estimate was observed when the ratio Ki/Kj was 1 or less. However, Ki/Kj ratios of 10 or higher caused Ki bias when impurity level exceeded 2.5%. No fit was obtained for Ki 500 µM (data not shown).

Impurity Effect on Active Uptake Kinetic Estimates: Experimental Evidence. A series of uptake experiments where model substrate GCA was contaminated with model impurities TLCA, CDCA, or UDCA were conducted. GCA K_t is approximately 10 µM, such that K_t/K_i for TLCA, CDCA, and UDCA were approximately 100, 10, and 1, respectively (Balakrishnan et al., 2006b). TLCA was found to be a competitive inhibitor of GCA uptake (Supplemental Fig. f).

Figure 5 shows bias on GCA K_t and J_max estimates from uptake when TLCA was present as impurity. These estimates were always negatively biased by the presence of TLCA at the entire range of X_i studied. Unbiased K_t and J_max of substrate were 12.6 (±1.1) µM and 0.424 (±0.041) pmol/cm²/s, whereas TLCA K_i was 0.11 (±0.01) µM. When X_i = 2%, observed K_t and J_max estimates of GCA were 3.76 (±0.87) µM and 0.113 (±0.004) pmol/cm²/s, representing an estimation error of 70 and 73% respectively. From Fig. 5A, interpolation predicts that a 0.24% mole fraction of TLCA would produce a 20% bias on GCA K_t estimate (experimental K_{t GCA}/K_{i TLCA} = 114), reflecting the high level of agreement between model predictions and observed results (r² = 0.979 for K_t and 0.995 for J_max).

View larger version (16K): [in this window] [in a new window]   Fig. 5. Impact of taurolithocholate on glycocholate kinetic estimates from uptake studies. Mole fraction of TLCA was varied from 0 to 10%. TLCA contamination produced negative bias on GCA kinetic estimates (i.e., increased "apparent" affinity and decreased "apparent" capacity in A and B, respectively). A 2% mole fraction of TLCA generated approximately 70% error on the estimation of both GCA Kt and Jmax. These experimental observations followed the model predicted trends. Unbiased Kt and Jmax of substrate were 12.6 (±1.1) µM and 0.424 (±0.041) pmol/cm2/s, whereas TLCA Ki was 0.11 (±0.01) µM. Data are presented as mean (±S.E.M.). S.E.M. bars are smaller than the symbol in B.

View larger version (17K): [in this window] [in a new window]   Fig. 6. Impact of chenodeoxycholate on glycocholate kinetic estimates from uptake studies. Experimentally, CDCA contamination produced modest negative bias on GCA Kt (A) and GCA Jmax (B), particularly compared to the larger bias shown in Fig. 5. This more modest level of bias reflects that CDCA impurity is less potent than TLCA impurity in Fig. 5. However, even a 10% mole fraction of CDCA impurity caused a 50% bias in estimated GCA Kt and Jmax (A and B, respectively). Unbiased Kt and Jmax of GCA substrate were 11.0 (±1.8) µM and 0.153 (±0.013) pmol/cm2/s, whereas CDCA Ki was 1.39 (±0.39) µM. Data are presented as mean (±S.E.M.).

Impurity Effect on Inhibition Constant Estimates: Experimental Evidence. To evaluate the impact of impurity on K_i estimates from inhibition assays, GCA-mediated inhibition of TCA uptake was measured in the absence and presence of TLCA as impurity (X_j = 0–10%). Here X_j represents the mole fraction of impurity J_imp (i.e., TLCA) contaminating inhibitor I (i.e., GCA) (Supplemental Fig. d). This inhibitor-impurity pair was chosen to represent a K_i/K_j 10 based on previous data (Balakrishnan et al., 2006b). Figure 7 shows bias on GCA K_i estimates as a function of TLCA mole fraction. Unbiased GCA K_i was 5.05 (±0.48) µM, whereas unbiased TLCA K_j was 0.40 (±0.04) µM(K_i/K_j = 12.6). TLCA contamination caused negative bias on GCA inhibition constant estimation over the entire range of X_j. For example, when TLCA was present at a 2% mole fraction, observed GCA K_i estimate was 4.39 (±0.32) µM. Interpolation of predicted data identified a critical X_j level of 1.95% to obtain a 20% bias on GCA K_i estimation. The impurity-present model predicted observed bias on K_i (r² = 0.919).

View larger version (13K): [in this window] [in a new window]   Fig. 7. Impact of taurolithocholate on glycocholate Ki when taurolithocholate was present as impurity. TLCA contamination produced negative bias on GCA apparent affinity. A 4% mole fraction of TLCA caused 34% error in GCA Ki estimation. Predicted bias anticipated these experimental observations. Unbiased Ki of GCA was 5.05 (±0.48) µM, whereas TLCA Kj was 0.40 (±0.04) µM. Data are presented as mean (±S.E.M.).

	Discussion

Top Abstract Materials and Methods Results Discussion Appendix A Appendix B References

 
Implications for ADME Screening and hASBT Studies. Transport and inhibition studies are routinely performed in early development to screen for ADME. A current project in our laboratory concerns the targeting of hASBT for drug delivery purposes (Balakrishnan and Polli, 2006). ADME considerations motivate the screening for substrates and inhibitors of hASBT to construct a QSAR model for inhibitors and substrates of this transporter.

hASBT (SLC10A2) is a 348 amino acid transmembrane protein that mediates the active uptake of bile acids in the small intestine, playing a critical role in the bile acid enterohepatic recirculation (Hagenbuch and Dawson, 2004). The total bile acid pool in humans (3–5 g) recirculates several times a day, giving a turnover of 12 to 18 g/day (Hofmann, 1999). However, no more than 0.5 g are lost in the feces daily, reflecting the high capacity and efficiency of this transporter (Hofmann and Mysels, 1992). This suggests that some drugs with poor oral absorption may benefit from conjugation to bile acids by utilizing hASBT as carrier to enter the enterocyte. Despite the enormous potential of hASBT as target for bile acid containing prodrugs, only a few examples of its use can be found in the literature (Sievänen, 2007). Employing this approach, the oral bioavailability of acyclovir was enhanced in rats via a bile acid conjugate prodrug of acyclovir (Tolle-Sander et al., 2004). Furthermore, hASBT is a promising pharmacological target, where hASBT inhibitors could lower blood cholesterol (Buchwald et al., 2002). Hence, hASBT is a target for novel substrates and inhibitors. An understanding of impurity effects on transport and inhibition assays is needed and the subject of this report.

Since January 2007, the Journal of Medicinal Chemistry now requires that key target compounds possess a purity of 98% or more. Results here support this requirement and indicate that, in transport and uptake studies, impurity can cause an underestimation in J_max, as well as an underestimation in K_t. This impact on apparent K_t appears to be surprising, because impurity would cause the apparent affinity of a substrate to be more potent than its true potency. Results of these simulation studies imply that transport studies results that conclude a drug candidate to be a potent substrate merit inspection to assure that impurity is not causing over-favorable results, particularly if a chemical reactant, precursor, or side product is known to be a potent inhibitor.

For example, in employing hASBT as a carrier for drug delivery and a bile acid prodrug where TCA (K_i = 5 µM) is the targeting moiety, a result of K_t = 50 µM could reflect several scenarios, such as 1) the target compound to possess K_t = 50 µM; 2) the target to possess K_t = 500 µM but also be contaminated with a most strong impurity (K_i = 0.05 µM) at a level of X_i = 0.075%; 3) the target to possess K_t = 500 µM but also be contaminated with a very strong impurity (K_i = 0.5 µM) at a level of X_i = 0.8%; or 4) the target to possess K_t = 500 µM but also be contaminated with an impurity (K_i = 5 µM) at a level of X_i = 8%.

Experience to date suggests that scenario "1" would be most likely, which is favorable as the intent is to measure unbiased parameters, but emphasizes that purification methods should be designed to remove critical impurities from target compound. Scenario "2" does not appear likely, because the lowest K_i to date is 0.5 µM or approximately 10-fold less potent than required by this scenario. Scenario "4" is not practically possible, because this scenario would require at least 8% impurity by TCA, which will not occur with purification effort. Scenario "3" represents a potentially real and challenging situation, where a relatively small amount (i.e., X_i = 0.8%) of most potent impurity (e.g., TLCA with K_i = 0.5 µM) contaminates the target. However, formation of TLCA from unreacted taurocholate is not expected. Consideration of these scenarios supports a 97.5 to 98% purity level, as long as very potent impurities are not present. For target compounds of high interest, inhibition data showing a drug candidate to be potent inhibitor merit inspection in which impurity is not causing over-favorable results, particularly if a chemical reactant, precursor, or side product is known to be a potent inhibitor.

Results from this study motivates purification methods to eliminate, if not minimize, unreacted bile acid in target bile acid prodrug compounds. Furthermore, in the case of conjugates of highly potent bile acids, enough hydrolytic stability must be assured in the transport/inhibition buffer so that regeneration of the parent-targeting moiety does not occur during the course of the assay. Unfortunately, target compounds that show moderate or weak affinity cannot be completely excluded from potential bias, as small amounts of very strong impurity can bias results and evade conventional detection. Hence, the ultimate benefit of these findings may be the need for careful consideration of impurity effect on transport and inhibition results, particularly when QSAR analysis cannot explain high compound potency.

In conclusion, the present study concerns two types of ADME transport studies: inhibition studies and transport/uptake studies. It is presumed that in a competitive binding study (e.g., inhibition study), impurity with a potency greater than test compound potency may cause test compound to appear more potent than it is in actuality. This expectation was found to be correct here and offers quantitative guidelines. Surprisingly, an expectation that a potent impurity would diminish the apparent potency of a test compound in the uptake assay (i.e., increase Michaelis-Menten K_t) was found in this study to be incorrect. Rather, potent impurity, which reduces test compound flux, resulted in test compound appearing to possess higher substrate affinity (i.e., exhibit a lower K_t). This study provides quantitative guidelines, which are currently lacking, on maximal impurity levels to avoid bias on transporter parameter estimates (i.e., K_t, J_max, and K_i) in early drug development. Results have implications for other types of early discover assays, such as pharmacologic binding studies.

	Appendix A

Top Abstract Materials and Methods Results Discussion Appendix A Appendix B References

 
Derivation of Influence of Impurity on Solute Flux. The objective of this appendix is to derive eq. 1, which models the impact of impurity on solute flux in transport studies. For the flux of a solute across a monolayer, where solute is translocated both passively and actively, but where inhibitor is present to inhibit active solute flux,

(6)

where J_mono is total flux across cell monolayer, S is concentration of solute, I is the concentration of inhibitor, P_p is the passive permeability of solute, K_t and J_max are the Michaelis-Menten constants for active transport, and K_i is the inhibitor affinity of I. Inhibitor I is an impurity present in the solute S. Only a single impurity is present. For example, in the design of bile acid conjugates to serve as prodrugs to target hASBT, the bile acid conjugate is the solute but can be contaminated with unreacted bile acid, which was a starting material in conjugate synthesis.

The mole fraction impurity in the sample is

(7)

where X_i is mole fraction of impurity in the sample.

(8)

Substituting eq. 8 into eq. 6,

(9)

The flux in eq. 9 is the flux across a monolayer where solute is both actively and passively translocated but where an impurity inhibits active solute transport. It should be emphasized that S is actual solute concentration. From eq. 9, the monolayer permeability can be considered to be

(10)

Given that the flux across a monolayer is in series with the aqueous boundary layer (ABL),

(11)

where P_app is apparent permeability and P_ABL is the permeability of S across the ABL. Substituting eq. 10 into eq. 11,

(12)

(13)

With impurity present in solute, the apparent flux is J_Xi = P_app x S, such that

(14)

Equation 14 describes the flux of a solute across a monolayer, in the presence of an ABL, where solute is translocated actively and passively and where impurity inhibits solute active transport.

If no impurity is present or if impurity does not inhibit solute active transport, eq. 14 simplifies to

(15)

Equations 14 and 15 are eqs. 1 and 2 under Materials and Methods and represent the impurity-present transport model and impurity-absent transport model, respectively.

	Appendix B

Top Abstract Materials and Methods Results Discussion Appendix A Appendix B References

 
Derivation of Influence of Impurity on Apparent Inhibitor Performance. The objective of this appendix is to derive eq. 3, which models the impact of impurity on apparent inhibitor performance in inhibition studies. This derivation employs a Michalis-Menten approach to the scenario where impurity inhibits transporter function. Supplemental Fig. d represents the inhibition of a transporter E by two mutually exclusive inhibitors, I and J_imp, where S is a substrate, I is an inhibitor, and J_imp is an impurity of I, which also inhibits E.

Let K_t = (k_–1 + k₂)/k₁

where K_t, K_i, and K_j are denoted affinity constants of S, I, and J_imp for the transporter. If k_–'₁ >> k₂' (i.e. rapid equilibrium),

Hence, K_i = (E x I)/EI

(16)

If k_–''₁ >> k₂'',

Hence, K_j = (E x J_imp)/EJ_imp

(17)

The rate of translocation of substrate S is,

(18)

and the total concentration of transporter at any time is

(19)

dividing eq. 18 by eq. 19

(20)

(21)

where J_max = k₂ x E_t is the maximal flux when transporter is saturated.

At steady state,

(22)

Hence,

(23)

Substituting K_t into eq. 23,

(24)

Substituting eqs. 16, 17, and 24 into eq. 21,

(25)

which expresses the rate of translocation of substrate S by a transporter in presence of two mutually exclusive inhibitors.

Allowing passive flux of solute across a monolayer, eq. 25 yields

(26)

where P_p is the passive permeability of solute across the monolayer.

Let X_j = J_imp/(J_imp + I) be the mole fraction of inhibitor J_imp contaminating inhibitor I. For example, I is a prodrug synthesized by conjugation of a bile acid and a drug, J_imp is an impurity (e.g. unreacted bile acid) that is also an inhibitor of hASBT.

(27)

Substituting eq. 27 into eq. 26 and considering the presence of an ABL (see Appendix A),

(28)

where is J_Xj is the total flux of a solute S (both active and passive) in the presence of two inhibitors when an ABL is considered. If impurity J_imp is not present or if it does not inhibit the transporter, eq. 28 simplifies to

(29)

Equations 28 and 29 are eqs. 3 and 4 under Materials and Methods and represent the impurity-present inhibition model and impurity-absent inhibition model, respectively.

	Footnotes

 
This work was support in part by National Institutes of Health Grant DK67530 (to J.E.P.).

Article, publication date, and citation information can be found at http://jpet.aspetjournals.org.

doi:10.1124/jpet.107.135863.

ABBREVIATIONS: ADME, absorption, distribution, metabolism, and excretion; hASBT, human apical sodium-dependent bile acid transporter; SLC, solute carrier family; MDCK, Madin-Darby canine kidney; HBSS, Hanks' balanced salt solution; ABL, aqueous boundary layer; QSAR, quantitative-structure activity relationship; CDCA, chenodeoxycholic acid; GCA, glycocholic acid; TCA, taurocholic acid; TLCA, taurolithocholic acid; UDCA, ursodeoxycholic acid.

The online version of this article (available at http://jpet.aspetjournals.org) contains supplemental material.

Address correspondence to: Dr. James E. Polli, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, HSF2 Room 623, Baltimore, MD 21201. E-mail: jpolli{at}rx.umaryland.edu

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Top Abstract Materials and Methods Results Discussion Appendix A Appendix B References

 

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