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 Kt and Jmax and inhibition Ki. 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 Ki). 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 Kt). 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.
Transport and inhibition studies are routinely performed in early development to screen for absorption, distribution, metabolism, and excretion (ADME). For example, a current project in our laboratory concerns the targeting of an intestinal transporter for drug delivery purposes (Balakrishnan and Polli, 2006). The transporter is the human apical sodium-dependent bile acid transporter (hASBT). ADME considerations in this project motivate the screening for substrates and inhibitors of hASBT to construct a quantitative structure-activity relationship (QSAR) model for inhibitors and substrates of this transporter. Test compounds are currently being synthesized to evaluate the chemical structural features that allows for hASBT inhibition, as well as translocation by hASBT (Balakrishnan et al., 2006a).
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 Kt) 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 Kt). This study provides quantitative guidelines, which are currently lacking, on maximal impurity levels to avoid bias on transporter parameter estimates (i.e., Kt, Jmax, and Ki) in early drug discovery. Results have implications for other types of early discover assays, such as pharmacologic binding studies.
Materials and Methods
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, Xi, of 2, 4, 6, 8, and 10%, respectively. Ki 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, Xj, of 2, 4, 6, 8, and 10%, respectively. TLCA inhibition of TCA uptake was also measured to obtain Kj (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 Ki 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.
Flux in the presence of impurity (JXi) was calculated using eq. 1 over a range of Kt, Xi, and Ki values. Substrate Kt was 5, 50, and 500 μM; substrate concentration (S) was one-tenth of Kt. Impurity level (Xi) was varied from 0 to 10% mole fraction, with greater sampling for larger Kt scenarios. Ki was 0.05, 0.5, 5, 50, and 500 μM, respectively. Jmax, PABL, and Pp were fixed to 0.5 pmol/cm2/s, 70 × 10–6 cm/s, and 0.5 × 10–6 cm/s, respectively, in all cases (Balakrishnan et al., 2007). Equation 2 (i.e., flux without impurity, J) is eq. 1 when Xi = 0. The ratio JXi/J was calculated as a metric for impurity effect on flux and plotted against Xi. It is important to note that S was the actual assigned substrate concentration in donor; the concentration of substrate and impurity (when Xi > 0) was greater than S.
Simulation of Substrate Transport: Impurity Effect onKt andJmax Estimates. To assess impurity effect on Kt and Jmax, estimates from transport studies, simulated flux data were generated from impurity-present model (eq. 1). Across the simulations, Kt was 5, 50, or 500 μM, whereas Jmax was 0.5 pmol/cm2/s. Because the aim of these simulation studies is to measure impurity effect on bias on estimated Kt and Jmax parameter fits, these Kt values (i.e., 5, 50, and 500 μM) and Jmax value (i.e., 0.5 pmol/cm2/s) are denoted as “true Kt” and “true Jmax”, respectively. In simulations, S was 1/20, 1/10, 1/5, 1/2, 1, 2, 5, 10, and 20 × Kt to assure saturation of active transport. PABL and Pp were 70 × 10 6– and 0.5 × 10–6 cm/s, respectively. Simulated flux data were subsequently fitted to impurity-absent model (eq. 2) for each unique condition (i.e., unique Kt, Ki, and Xi scenario). Nonlinear regression was used to simultaneously estimate Kt and Jmax using SigmaPlot 8.0 (SPSS Inc, Chicago, IL). In all cases, r2 = 1.000. Results are discussed in terms of resulting bias in Kt and Jmax, due to impurity, relative to “true Kt” and “true Jmax” values that were employed in simulating flux data. Kt estimates were plotted against impurity level for each “true Ki” level. Similar plots were graphed for Jmax estimates. Estimation error in Kt (or Jmax) that exceeded 20% was considered unacceptably biased.
Simulation of Inhibition Studies: Impurity Effect onKi Estimate. To simulate impurity influence on Ki 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.
It should be noted that, in contrast to transport simulations above where Ki is the inhibition constant of the impurity that contaminates the substrate, Ki is the unbiased inhibition constant of the inhibitor (i.e., the value to be measured in the inhibition study). Kj is the unbiased inhibition constant of the impurity that contaminates the inhibitor. Impurity is present in inhibitor at level Xj. Across simulations, Kt, Jmax, PABL, and Pp were fixed to be 5 μM, 0.5 pmol/cm2/s, 70 × 10–6 cm/s, and 0.5 × 10–6 cm/s, respectively, which reflects active TCA transport across hASBT-MDCK monolayers (Balakrishnan et al., 2007). Ki 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 × Ki. Impurity Kj was 0.5, 5, and 50 μM. An entire inhibition profile was generated for each level of impurity (Xj), which ranged from 0 to 10% mole fraction. Each inhibition profile (i.e., unique Ki, Kj, and Xj scenario) was fitted to impurity-absent inhibition model (eq. 4; see Appendix B) using nonlinear regression. Only Ki 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 (Ki = 500 μM) where r2 < 0.6.
Results are discussed in terms of resulting bias in Ki, due to impurity, relative to true Ki values that were employed in simulating inhibition profile. Ki estimates were plotted against impurity level for each true Kj level. Estimation error in Ki 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 Kt to be 11.0 ± 1.9 μM. Ki 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 Kt/Ki ≈ 100, 10, and 1, respectively.
The uptake format was chosen to keep Pp 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, PABL was set to 1.5 × 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 (Xi = 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 Kt, Jmax, Ki, and Pp. Subsequently, values of these unbiased estimates were applied to eq. 1 (i.e., impurity-present uptake model) to simulate GCA-uptake profiles when Xi was 2, 4, 6, 8, and 10%, respectively. Predicted “biased” Kt and Jmax were obtained by nonlinear fitting of these subsequent curves to eq. 2.
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, Kt and Jmax, for each level of impurity mole fraction, Xi. 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 Ki. 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 Ki, Kj, Jmax, and Pp. TCA Kt 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 Xj was 2, 4, 6, 8, and 10%, respectively. Predicted biased Ki 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 Ki for each level of impurity mole fraction, Xj. Predicted bias and observed bias were compared.
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., Kt = 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., Ki = 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., Ki = 0.05 μM; closed circles) was needed to cause a 20% decrease in flux. Simulations covering a broader range of Kt values showed similar trends, including the susceptibility of weaker substrates to more pronounced impurity effects (see Supplemental Fig. a).
Figure 2 illustrates the effect of impurity potency on flux profile of a strong substrate (Kt = 5 μM). Impurity level was Xi = 2%. In general, greater inhibition potency of impurity resulted in reduced substrate flux. For example, a most strong impurity (Ki = 0.05 μM) reduced substrate flux approximately 3-fold at 50 μM substrate concentration. Of note, an impurity whose Ki is equal to the substrate's Kt (i.e., Ki = 5 μM) had no marked effect. Meanwhile, the impurities with 10- (i.e., Ki = 0.5 μM) and 100-fold (i.e., 0.05 μM) greater potency provided notable flux reduction. Qualitatively, similar results were observed for the effect of impurity potency on moderate (Kt = 50 μM) and weak (Kt = 500 μM) substrate (data not shown), although the effects are more dramatic.
Simulation of Substrate Transport: Impurity Effect onKt andJmax Estimates. The impact of impurity on Kt and Jmax 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 Kt and Jmax. Whereas this effect in Jmax is intuitive, this effect on Kt 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 Kt < true Kt).
Figure 3 illustrates estimated Kt 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 Kt/Ki = 10, the impurity level needed to cause bias was always approximately 2.5% mole fraction. For example, Xi = 2.5% lead the estimated Kt to be 4 μM when Kt/Ki = 10. Simulations covering a broader range of Kt values showed similar trends, including the susceptibility of weaker substrates to impurity effects (see Supplemental Fig. b)
Table 2 summarizes the relationship between Kt/Ki ratio and the necessary Xi to cause bias. In Table 2, regardless whether the potency of the substrate was 5, 50, or 500 μM, the Kt/Ki ratio of 10 always resulted in Kt bias, although bias required the impurity level to be at least 2.5% mole fraction. When Kt/Ki = 100, approximately 0.25% impurity caused 20% bias, depending on the Kt value. However, when Kt/Ki = 1 or less, bias did not manifest for even Xi = 10%.
Regarding effect of impurity on Jmax, plots of estimated Jmax versus Xi were identical to those in Fig. 3. It is interesting that Xi levels had the same relative effect on Jmax as on Kt (Supplemental Fig. c). Although presented for impurity effect on Kt bias, Table 2 equally applies for Jmax bias. For example, when Kt/Ki = 1 or less, bias in Jmax did not manifest.
Whereas the effect on Jmax is intuitive, this effect of impurity to reduce Kt would appear to be unexpected. Kt 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 Kt < true Kt). Figure 2 illustrates the basis for this effect. Figure 2 was generated for Xi = 2% for a strong substrate (Kt = 5 μM). For the scenario Ki = 0.5 μM (open circles), estimated Jmax and Kt 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 Jmax estimate causes a proportional effect on Kt estimate, vis-à-vis the Jmax effect. Given that Kt is the concentration at half-Jmax and because impurity reduces apparent Jmax, impurity reduces apparent Kt.
Simulation of Inhibition Studies: Impurity Effect onKi 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 (Ki). Here, impurity Jimp 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 Ki estimates from inhibition studies.
The extent of this bias is illustrated in Fig. 4 (substrate Kt = 5 μM) for a strong inhibitor (Ki = 5 μM) and shows bias depended on the affinity of the impurity (i.e., Kj) and its mole fraction, Xj. For example, a very strong impurity (Kj = 0.5 μM) at Xi = 2.5% caused 20% bias in estimated Ki, resulting in apparent Ki to be 4 μM. Simulations covering a broader range of Ki 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.
Table 3 summarizes the relationship between Ki/Kj ratio and the impurity level required to produce bias on Ki estimates. Results in Table 3 from inhibition studies mimic results in Table 2 from transport studies, although Table 3 concerns Ki/Kj ratio, whereas Table 2 concerns Kt/Ki ratio. In Table 3, no bias manifested when Ki/Kj ≤ 1, similar to an observation in Table 2. In addition, for either a strong or moderate inhibitor, Ki/Kj = 10 resulted in bias when Xj was at least 2.5%, similar to an observation in Table 2. For a moderate inhibitor, Xj = 0.6% caused bias in Ki when Ki/Kj = 100.
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 Kt is approximately 10 μM, such that Kt/Ki 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 Kt and Jmax 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 Xi studied. 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. When Xi = 2%, observed Kt and Jmax 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 Kt estimate (experimental Kt GCA/Ki TLCA = 114), reflecting the high level of agreement between model predictions and observed results (r2 = 0.979 for Kt and 0.995 for Jmax).
Identical experiments were performed with GCA contaminated with CDCA as impurity. Unbiased GCA Kt and Jmax were 11.0 (±1.8) μM and 0.153 (±0.013) pmol/cm2/s, respectively, whereas unbiased CDCA Ki = 1.39 (±0.39) μM(Kt/Ki = 7.92). Although CDCA caused negative bias on GCA Jmax, regardless of impurity load, Kt estimates were biased only when Xi > 2%. Figure 6 shows the bias effect of CDCA on GCA Kt and Jmax. Experimentally, a 6% mole fraction of CDCA caused a 20% drop on estimated GCA Kt relative to the unbiased value, whereas 4% caused similar estimation error on Jmax. The ability of the model to predict biased Kt estimates was modest (r2 = 0.806) and good to predict Jmax (r2 = 0.908).
Identical studies were also performed with GCA-UDCA as substrate-impurity pair. Unbiased GCA Kt was 9.82 (±0.79) μM. Unbiased UDCA Ki was 24.2 (±2.8) μM, such that Kt/Ki = 0.40 for this GCA-UDCA pair. Unlike the above GCA-TLCA and GCA-CDCA pairs, it was not possible to obtain biased estimates for the GCA-UDCA pair due to poor inhibitory potency of the UDCA impurity.
Impurity Effect on Inhibition Constant Estimates: Experimental Evidence. To evaluate the impact of impurity on Ki estimates from inhibition assays, GCA-mediated inhibition of TCA uptake was measured in the absence and presence of TLCA as impurity (Xj = 0–10%). Here Xj represents the mole fraction of impurity Jimp (i.e., TLCA) contaminating inhibitor I (i.e., GCA) (Supplemental Fig. d). This inhibitor-impurity pair was chosen to represent a Ki/Kj ≈ 10 based on previous data (Balakrishnan et al., 2006b). Figure 7 shows bias on GCA Ki estimates as a function of TLCA mole fraction. Unbiased GCA Ki was 5.05 (±0.48) μM, whereas unbiased TLCA Kj was 0.40 (±0.04) μM(Ki/Kj = 12.6). TLCA contamination caused negative bias on GCA inhibition constant estimation over the entire range of Xj. For example, when TLCA was present at a 2% mole fraction, observed GCA Ki estimate was 4.39 (±0.32) μM. Interpolation of predicted data identified a critical Xj level of 1.95% to obtain a 20% bias on GCA Ki estimation. The impurity-present model predicted observed bias on Ki (r2 = 0.919).
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 Jmax, as well as an underestimation in Kt. This impact on apparent Kt 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 (Ki = 5 μM) is the targeting moiety, a result of Kt = 50 μM could reflect several scenarios, such as 1) the target compound to possess Kt = 50 μM; 2) the target to possess Kt = 500 μM but also be contaminated with a most strong impurity (Ki = 0.05 μM) at a level of Xi = 0.075%; 3) the target to possess Kt = 500 μM but also be contaminated with a very strong impurity (Ki = 0.5 μM) at a level of Xi = 0.8%; or 4) the target to possess Kt = 500 μM but also be contaminated with an impurity (Ki = 5 μM) at a level of Xi = 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 Ki 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., Xi = 0.8%) of most potent impurity (e.g., TLCA with Ki = 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 Kt) 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 Kt). This study provides quantitative guidelines, which are currently lacking, on maximal impurity levels to avoid bias on transporter parameter estimates (i.e., Kt, Jmax, and Ki) in early drug development. Results have implications for other types of early discover assays, such as pharmacologic binding studies.
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, where Jmono is total flux across cell monolayer, S is concentration of solute, I is the concentration of inhibitor, Pp is the passive permeability of solute, Kt and Jmax are the Michaelis-Menten constants for active transport, and Ki 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 where Xi is mole fraction of impurity in the sample.
Substituting eq. 8 into eq. 6,
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
Given that the flux across a monolayer is in series with the aqueous boundary layer (ABL), where Papp is apparent permeability and PABL is the permeability of S across the ABL. Substituting eq. 10 into eq. 11,
With impurity present in solute, the apparent flux is JXi = Papp × S, such that
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
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.
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 Jimp, where S is a substrate, I is an inhibitor, and Jimp is an impurity of I, which also inhibits E.
Let Kt = (k–1 + k2)/k1 where Kt, Ki, and Kj are denoted affinity constants of S, I, and Jimp for the transporter. If k–′1 ≫ k2′ (i.e. rapid equilibrium),
Hence, Ki = (E × I)/EI
If k–″1 ≫ k2″,
Hence, Kj = (E × Jimp)/EJimp
The rate of translocation of substrate S is, and the total concentration of transporter at any time is dividing eq. 18 by eq. 19 where Jmax = k2 × Et is the maximal flux when transporter is saturated.
At steady state,
Substituting Kt into eq. 23,
Substituting eqs. 16, 17, and 24 into eq. 21, 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 where Pp is the passive permeability of solute across the monolayer.
Let Xj = Jimp/(Jimp + I) be the mole fraction of inhibitor Jimp contaminating inhibitor I. For example, I is a prodrug synthesized by conjugation of a bile acid and a drug, Jimp is an impurity (e.g. unreacted bile acid) that is also an inhibitor of hASBT.
Substituting eq. 27 into eq. 26 and considering the presence of an ABL (see Appendix A), where is JXj 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 Jimp is not present or if it does not inhibit the transporter, eq. 28 simplifies to
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.
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.
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 American Society for Pharmacology and Experimental Therapeutics