Abstract
For therapeutic biologics against soluble ligands, the magnitude and duration of target suppression affect their therapeutic efficacy. Many factors have been evaluated in relation to target suppression but the interstitial fluid turnover rate in target tissues has not been considered. Inspired by the fact that etanercept exerts limited efficacy in Crohn’s disease despite its high efficacy in rheumatoid arthritis, we developed a minimal physiologically based pharmacokinetic model to investigate the role of the tissue fluid turnover rate on soluble target suppression and assessed the interrelationships between binding constants and tissue fluid turnover. Interstitial fluid turnover rates in target tissues were found to strongly influence target binding kinetics. For tissues with low fluid turnover, stable binders (low koff) exhibit greater target suppression, but efficacy is often restricted by accumulation of the drug-target complex. For tissues with high fluid turnover, fast binders (high kon) are generally favored, but a plateau effect is present for antibodies with low dissociation rates (koff). Etanercept is often regarded as a fast tumor necrosis factor-α (TNF-α) binder (high kon) despite comparable binding affinity (KD, koff/kon) with adalimumab and infliximab. Crohn’s disease largely involves the colon, a tissue with relatively slower fluid turnover than arthritis-associated joint synovium; this may explain why etanercept exerts poor TNF-α suppressive effect in Crohn’s disease. This study highlights the importance of tissue interstitial fluid turnover in evaluation of therapeutic antibodies bound to soluble antigens.
Introduction
Several therapeutic biologics that neutralize or block soluble proinflammatory cytokine tumor necrosis factor-α (TNF-α) have demonstrated strong efficacy in the treatment of autoimmune diseases, such as rheumatoid arthritis (RA) and Crohn’s disease (CD) (Feldmann et al., 1996; Pizarro and Cominelli, 2007; van Schouwenburg et al., 2013). Monoclonal antibodies (mAbs), such as adalimumab and infliximab and fusion protein etanercept, all exhibit high specificity and affinity for binding to soluble TNF-α and display therapeutic efficacy for RA (Garrison and McDonnell, 1999; Markham and Lamb, 2000; Bang and Keating, 2004). Surprisingly, when using these biologics for CD, another autoimmune disease in which TNF-α is a critical pathogenic factor, only adalimumab and infliximab (but not etanercept) show sufficient efficacy (Olesen et al., 2016). The reason for this difference have focused on the distinct binding affinities of these biologics to transmembrane TNF-α and the role of transmembrane TNF-α in the pathogenesis of CD (Tracey et al., 2008; Horiuchi et al., 2010). Unfortunately, it remains unclear why there is limited efficacy of etanercept in treatment of CD.
For therapeutic biologics that bind to soluble ligands, their efficacy is largely determined by the magnitude and duration of ligand suppression (Wang et al., 2008). According to previous reports (Kim et al., 2007; Song et al., 2008), adalimumab and infliximab have relatively slower association and dissociation rates than etanercept when binding to soluble TNF-α, even though all biologics exhibit comparable binding affinities (KD) in the subnanomolar range. In addition, compared with etanercept, infliximab and adalimumab frequently form large immune complexes once bound to one or more trimers of soluble TNF-α (Scallon et al., 2002). Such large immune complexes may be responsible for their high risk of tuberculosis (Fallahi-Sichani et al., 2012). For etanercept, such large immune complexes have been seldom reported and the risk of tuberculosis is relatively low (Cantini et al., 2014). There is similar efficacy among the three biologics for RA (Aaltonen et al., 2012). It remains a puzzle why etanercept lacks sufficient efficacy for CD, whereas the other two biologics exhibit strong effect. Here, a modeling approach was used to explore the underlying mechanisms, particularly focusing on differences in TNF-α binding kinetics and the role of interstitial fluid (ISF) turnover.
For many antibody-based biologics, the interstitial space within the diseased tissues is their actual sites of action. Tissue ISF is generally believed to be the major extravascular distribution space for antibody-based biologics (Cao et al., 2013; Eigenmann et al., 2017). Vascular convection drives antibody distribution into ISF and then lymphatic drainage provides removal from tissue ISF back into the circulation. The efficiency of lymphatic drainage in target tissues, which essentially determines the ISF turnover rate, is expected to affect antibody-target binding kinetics and binding equilibrium, and might further influence the magnitude and duration of target suppression (Guo et al., 2009; Zhou et al., 2011; Becker et al., 2015). The ISF turnover varies greatly among tissues. For antibodies bound to soluble antigens, evaluating the influence of tissue ISF turnover on target suppression and therapeutic efficacy in disease-associated tissues is warranted.
The second-generation minimal physiologically based pharmacokinetic (mPBPK) model accommodates the fundamental distribution mechanisms of mAbs or fusion proteins, such as lymphatic convection and drainage, and ISF as the primary extravascular distribution space (Cao et al., 2013; Cao and Jusko, 2014b). This modeling approach provides a reasonable approximation of antibody concentrations in ISF, and allows incorporating antibody-target binding kinetics in plasma and/or tissue interstitium, or any other disease-associated tissues (Cao and Jusko, 2014a; Chen et al., 2016, 2017). The present assessment extends the original mPBPK model with a specific disease-associated tissue, where the dynamic ISF turnover was defined. The impact of tissue ISF turnover on TNF-α suppression among three TNF-α antagonists (etanercept, adalimumab, and infliximab) was evaluated to test whether the differences of tissue ISF turnover would explain the limited efficacy of etanercept in CD. Antigen binding kinetics in target tissues for biologics with soluble targets was also explored to test the influence of tissue fluid turnover on target binding kinetics and target suppression. This concept was applied to survey 27 licensed antibodies that bind to soluble ligands for various indications and to evaluate how fluid turnover of their target tissues influenced their efficacy.
Materials and Methods
An Extended mPBPK Model.
As shown in Fig. 1, a second-generation mPBPK model is proposed with a specific disease-associated tissue compartment to assess the impact of tissue ISF turnover on the therapeutic efficacy of biologics that bind to soluble antigens. The mPBPK model is described by the following equations:(1)
(2)
(3)
(4)where Cp and Clymph are the mAb concentrations in Vp (plasma volume) and Vlymph (lymph volume), respectively, in a 70 kg man; Ctight and Cleaky are the mAb ISF concentrations in two groups of lumped tissues categorized by continuous and fenestrated vascular endothelium structures; and Vtight (0.65·ISF·Kp, where ISF is total system ISF and Kp is the available fraction of ISF for distribution) and Vleaky (0.35·ISF·Kp) indicate the ISF volumes for tight tissue (muscle, skin, adipose, and brain) and leaky tissue (all other tissues such as liver, kidney, heart, etc.) (Cao et al., 2013). Here, L is the total lymph flow, which is equal to the sum of L1 (lymph flow for Vtight) and L2 (lymph flow for Vleaky), where L1 = 0.33 × l and L2 = 0.67 × l (Shah and Betts, 2012); σ1 and σ2 are the vascular reflection coefficients for Vtight and Vleaky, which are fixed at 0.95 and 0.71 (Cao and Jusko, 2014b); σL is the lymphatic reflection coefficient, which is assumed to be 0.2 in this model (Garg and Balthasar, 2007); and CLp is the systemic clearance from plasma (Cao and Jusko, 2014b). All initial conditions equal zero except for Cp, which is equal to 1000 nM. In this model, the physiologic parameters (Davies and Morris, 1993) for a 70 kg body weight person are Vp = 2.6 l, Vlymph = 5.2 l, L = 2.9 l/day, ISF = 15.6 l, and Kp was set to 0.8 in this case (Wiig et al., 1994; Wiig and Tenstad, 2001). Here, Cp (0) = 1000 nM was selected within the plasma concentration ranges of many mAbs at their therapeutic doses, which is equivalent to the peak concentrations of antibody in a 70 kg man after being given about 5 mg/kg dose of antibody.
Second-generation mPBPK model extended with target binding kinetics and ISF turnover in target tissues. The plasma compartment (dashed line) on the left is exactly the same compartment as the plasma compartment on the right. This layout of model structure is consistent with that for full physiologically based pharmacokinetic models. The dashed line of recycled antibody in the target tissue compartment indicates that there is no recycled antibody considered back into the lymph node compartment since the recycled antibody and antibody-target complex account for only a small fraction of total antibody. Symbols are defined in Table 1 and physiologic restrictions are defined in Eqs. 1–7.
Within the target tissue compartment, the target-mediated drug disposition component was defined for the binding kinetics between biologics and soluble TNF-α. The differential equations are(5)
(6)
(7)where A, Tar, and ATar are the concentrations for the antibody, free-target, and antibody-target complex. Parameters kon and koff are the association and dissociation rate constants; ksyn and kdeg represent the target biosynthesis and degradation rates; and Tar (0) is defined as the target baseline (= ksyn/kdeg). All initial conditions equal zero except for Tar, which equals 100%. The tissue ISF turnover rate was derived from the equation
, where LT is the lymph flow and VT is the ISF volume for a target tissue. The values for LT and VT are assumed to be 2% of the system lymph flow and 2% of the total ISF volume, respectively. All system parameters employed are listed in Table 1.With the binding parameters of etanercept, we simulated a range of tissue ISF turnover (0.001–100 hour−1), which roughly represented the range of ISF turnover in human tissues. The influence of tissue ISF turnover on concentrations versus time profiles of antibody, free target, and antibody-target complex in target tissues was simulated.
Model parameters employed in the extended mPBPK model simulations
Influence of Tissue ISF Turnover Rate on TNF-α Suppression.
The binding kinetic parameters to human soluble TNF-α were obtained from the literature (Kim et al., 2007; Song et al., 2008), where the kon (×105 M−1·s−1) values were 2.59, 0.57, and 1.33 and the koff (×10−4⋅s−1) values were 13.1, 1.1, and 0.73 for etanercept, infliximab, and adalimumab, respectively. All target binding parameters are summarized in Table 2.
Properties related to 27 surveyed therapeutic antibodies
The area under the target concentrations [Tar(0) − Tar] versus the time curves (Δ area under the curve) for the three TNF-α antagonists were simulated with a range of ISF turnover rates, which covered the ranges for the colon (in CD) and joint synovium (in RA). Since the three biologics have different pharmacokinetics characteristics, degrees of tissue distribution, and approved dosing regimens, it is difficult to accurately predict their target site concentration versus time profiles. Previous pharmacokinetics studies suggested that the three biologics may have similar therapeutic concentrations at their maintenance doses (Tracey et al., 2008). Herein, we assumed average antibody pharmacokinetics and tissue distribution kinetics for three biologics (Table 1). This approximation allowed us to specifically evaluate the influence of tissue fluid turnover rate and binding kinetic constants on target suppression. The role of tissue ISF turnover on TNF-α suppression was compared among the three biologics. The TNF-α suppressive effect in two target-associated tissues, joint synovium and colon, was particularly compared.
Interrelationships between Tissue Fluid Turnover and Target-Binding Constants.
For a dynamic range of tissue ISF turnover rates (0.001–100 hour−1), we simulated the influence of increasing kon or decreasing koff, two strategies that could enhance target binding affinity and potentially improve drug efficacy. The impact of binding constants (kon or koff) on tissue accumulation of drug-target complexes was also simulated for a dynamic range of ISF turnover rates.
Survey of Licensed Biologics.
In the development of therapeutic antibodies, once a viable target is identified a next step is to decide at which degree of binding affinity the antibody could achieve substantial target inhibition. To better understand the relationships between target binding affinity and ISF turnover rates, we surveyed 27 antibodies that have been approved with soluble targets for many types of diseases. The ISF turnover rates of disease-associated tissues were calculated from the equation LT/ISFT, where the LT and ISFT values were the lymph flow and ISF volume for specific tissues associated with drug indications collected from the literature (Bauer et al., 1933; Brown et al., 1991; Renkin and Wiig, 1994; Levick, 1998; Shah and Betts, 2012). The biologics adalimumab, belimumab, canakinumab, and infliximab, which have indications associated with multiple indications or tissues, were plotted separately for each indication-corresponding tissue. The ISF turnover rate of tumor is normally low considering the collapsed lymphatic system, which was thus set at 0.0001 hour−1. The values of the average binding affinity for each antibody and the ISF turnover rates of their target-associated tissues are listed in Table 2. The parameter Ka was used (reciprocal of Kd and calculated as kon/koff) to reflect the binding affinity. The target turnover values for each target in Table 2 are listed in Supplemental Table 1.
Simulation and Statistical Analysis.
Model simulations were performed using Berkeley Madonna (version 8.3.18; Berkeley Madonna Inc., University of California, Berkeley, CA). All of the graphs and the statistical analysis were performed using GraphPad Prism 6 software (San Diego, CA). The values on the plots are expressed as mean ± S.D.
Results
Influence of Tissue ISF Turnover on Target Binding Kinetics.
To evaluate the influence of tissue ISF turnover rate on antibody-target binding kinetics, tissue concentration versus time profiles of antibody, free-target, and antibody-target binding complex for different ISF turnover rates from 0.001 to 100 hour−1 were simulated (Fig. 2). When the ISF turnover rate is decreased from 100 to 0.001 hour−1, the amount of antibody that distributes into the target tissues becomes restricted, since lymphatic convection primarily determines antibody access into disease tissues (Fig. 2A). The distribution rate also becomes slower with a decrease in the tissue ISF turnover rate. Tissues with higher ISF turnover rates generally experience stronger target suppression (Fig. 2B), which is partly associated with greater antibody concentrations in these tissues (Fig. 2A). This is also largely ascribed to increased efficiency in removing antibody-target complexes from these tissues through fast lymphatic flow; efficient removal of complexes drive target binding kinetics toward complex formation (i.e., association). As shown in Fig. 2C, an antibody-target complex gradually accumulates with a decrease in the ISF turnover rate from 100 to 0.1 hour−1. Greater accumulation of a drug-target complex would disturb the binding equilibrium and drive complex dissociation. Once tissue ISF turnover becomes lower than 0.1 hour−1, the drug-target complex would start to decrease since there would be insufficient tissue antibodies for binding to the target.
Simulated target tissue concentration vs. time profiles of free antibody (A), free target (B), and antibody-target binding complex (C) at a dynamic range of tissue ISF turnover rates from 0.001 to 100 hour−1. Tissue ISF turnover rates greatly influence target binding kinetics. Tissues with high fluid turnover are expected to have higher target suppression, which is partly ascribed to increased distribution of antibody and high efficiency in removal of drug-target complexes.
Influence of Tissue ISF Turnover Rates on TNF-α Suppression.
Compared with infliximab and adalimumab, etanercept is generally regarded as a fast TNF-α binder since it exhibits a higher association rate (kon is 2- to 5-fold higher). In contrast, infliximab and adalimumab are regarded as stable TNF-α binders since they both have relatively lower dissociation rates (koff is much slower than that for etanercept). With such distinct binding kinetics, their TNF-α suppressive effects (Δ area under the curve) in target tissues with changing ISF turnover are shown in Fig. 3. For target tissues with relatively slow ISF turnover rate, i.e., shown on the left-hand side in Fig. 3, infliximab and adalimumab show higher TNF-α suppressive effects. Considering that infliximab and adalimumab are both able to form large immune complexes, their effective dissociation rates are even lower than that at 1:1 binding (Scallon et al., 2002). Thus, the actual TNF-α suppressive effects for infliximab and adalimumab are expected to be greater than what is shown in Fig. 3. Such large immune complexes are seldom observed during etanercept treatment, which would not improve its poor TNF-α suppressive effects in tissue with low fluid turnover. However, along with the increase in tissue ISF turnover, the target suppressive effect of etanercept gradually increases and even surpasses infliximab once the tissue ISF turnover rate is >1 hour−1.
Simulated interrelationships between tissue fluid turnover rate and the TNF-α suppressive effects [Δ area under the curve (AUC)] by three anti-TNF-α biologics at a dynamic range of tissue ISF turnover rates. Simulation was performed using the binding constants (see Table 1) of each biologic to soluble TNF-α at their therapeutic doses. For joint (in RA), etanercept shows comparable or slightly higher target suppression than adalimumab and inflixiamb. However, for colon (in CD), etanercept exhibits much less target suppression than the other two biologics.
Two target tissues, colon (for CD) and joint synovium (for RA) are shown in Fig. 3 with their reported ISF turnover rates (0.03–0.06 for colon and 0.15–1.0 hour−1 for joint synovium). In the colon, two stable binders, infliximab and adalimumab, are expected to exhibit much higher target suppression than the fast binder etanercept. In joint synovium, differences in target suppression become smaller, and etanercept may potentially exhibit even greater TNF-α suppression than infliximab. This simulation provides a theoretical explanation for the limited efficacy of etanercept in CD, even though all three biologics showed comparable efficacy in RA at their therapeutic doses.
Interrelationships between Tissue Fluid Turnover and Target-Binding Constants.
Optimization of the target-binding constants (kon and koff) is a critical step in the development of therapeutic antibodies. Here, we simulated the potential effect of altering the target-binding constants on target suppression for tissues with a dynamic range of ISF turnover rates. As shown in Fig. 4A, a gradual increase in kon produces greater target suppression, and there seems to be no capacity limitation in the simulated range, particularly in tissues with higher ISF turnover. On the other hand, a decrease in koff (Fig. 4B), a commonly applied engineering strategy, showed a saturation effect in target suppression. This saturation was particularly apparent in tissues with relatively high ISF turnover rates.
Influence of changing antibody-target binding kinetics (kon and koff) for a dynamic range of tissue ISF turnover rates on minimum concentrations of free target (A and B) and accumulation of antibody-target complex (C and D). There is a plateau effect by lowering koff to improve binding affinity, particularly for tissues with relatively slow ISF turnover rates.
As shown in Fig. 4, C and D, there is considerable accumulation of drug-target complexes in tissues with relatively slow ISF turnover rates. Stronger binding affinity produces increased accumulation of the drug-receptor complex. The increased complex accumulation in tissues with slow ISF turnover rates accounts for their lower target suppression (Fig. 4, A and B).
Four Scenarios Defined by Target Binding Affinity and Tissue ISF Turnover.
Considering that the tissue ISF turnover rate and target binding affinity both affect target inhibition, herein we have defined four general scenarios based on the following criteria: high ISF turnover rate (>0.0077 hour−1, the system average) and high target affinity (>1 nM−1); high ISF turnover rate and low target affinity (<1 nM−1); low ISF turnover rate and high target affinity (>1 nM−1); and low ISF turnover rate and low target affinity (<1 nM−1).
These scenarios are shown in Fig. 5A. For tissues with high ISF turnover rates, fast target binders are favored for target inhibition, as indicated in Figs. 3 and 4A. However, there would be a plateau effect at high affinity by lowering koff (Fig. 4B). For tissues with relatively low ISF turnover rates, on average the target suppressive effect would be weaker than for tissues with high ISF turnover rates (Fig. 2B; Fig. 3), where stable binders are generally favored for therapy. Decreasing koff is expected to be more efficacious than increasing kon to improve target suppression in tissues with low ISF turnover rates. However, both strategies encounter the problem of accumulation of drug-target complexes, which could shift the binding equilibrium toward complex dissociation (Fig. 4, C and D). To effectively enhance target suppression for tissues with slow ISF turnover rates, reduction of complex accumulation should be considered as a therapeutic strategy.
Four scenarios proposed based on the binding affinity (Ka, = kon/koff) and tissue fluid turnover rate. (A) For tissues with low fluid turnover, stable binders are generally favored but high affinity often confers increased accumulation of antibody-target complex; for tissues with high fluid turnover, fast binders are generally favored but lowering koff beyond fluid turnover rate produces a plateau effect. (B) Summary of 27 licensed antibodies that bind to soluble ligands for treatment of various diseases. Of these, 24 were approved for treatment of diseases that are associated with tissues with relatively high fluid turnover.
Surveyed Licensed Biologics.
For the 27 surveyed biologics, the average target binding affinity and the ISF turnover rates of their corresponding disease tissues are summarized in Table 2 and Fig. 5B. Based on the scenarios shown in Fig. 5A, whole body turnover, calculated from the ratio of the total body flow rate and systemic ISF volume (L/ISF = 0.0077 hour−1), is defined as the system average turnover rate and is used to demarcate tissues into two scenarios: low-turnover tissues (tumor, bone, and muscle) and high-turnover tissues (colon, skin, joint, lung, kidney, lymph node, and plasma). As shown in Fig. 5B, 24 out of 27 (∼90%) approved biologics are associated with diseases in tissues with high ISF turnover rates. In those tissues with high ISF turnover rates, fast binders with adequate target binding affinities are expected to achieve high target suppression and sufficient therapeutic efficacy. Antibody candidates with fast binding constants thus have a higher possibility of therapeutic success.
Discussion
Many therapeutic biologics, such as mAbs and infusion proteins, have been developed to neutralize soluble pathogenic ligands in disease-associated tissues (Wang et al., 2016). For those biologics, the magnitude and duration of target neutralization largely predicts their therapeutic efficacy. Optimization of antibody-target binding constants is an essential strategy to improve therapeutic efficacy (Stein and Ramakrishna, 2017; Tiwari et al., 2017). Inspired by the distinct efficacy of three anti-TNF-α biologics (adalimumab, infliximab, and etanercept) for CD, we used an extended mPBPK model to evaluate the influence of tissue ISF turnover rates on target suppression for biologics that neutralize soluble targets. The different TNF-α suppression effects of the three biologics were simulated at a dynamic range of tissue fluid turnover rates. Even though these biologics exhibit comparable TNF-α binding affinity at the subnanomolar range, adalimumab and infliximab are considered to be stable binders because of their relatively lower kon and koff values to soluble TNF-α compared with etanercept. These two stable binders were predicted to have greater target suppression than etanercept in the CD-site colon. On the other hand, etanercept is expected to have comparable or a slightly stronger TNF-α suppression than adalimumab and infliximab at the RA-site joint synovium. Therefore, the different tissue fluid turnover rates between the colon and joint synovium may explain why etanercept exhibits limited efficacy in CD.
In this study, an mPBPK model with target-mediated drug disposition in a target tissue compartment was developed to demonstrate the importance of tissue fluid turnover rates on the magnitude of target suppression. In the model, tissue degradation of the target-antibody complex was assumed negligible and the antibody-target complex exhibited similar disposition behavior as free antibody (Tiwari et al., 2017). In the developed model, the volume of ISF in the target tissue was assumed to be 2% of the total ISF (with the lymph flow being about 2% of the total lymph flow), which makes the amount of antibody contained in the target tissue not a significant factor in the system mass balance. For simplicity, the recycled antibody was not considered in the plasma compartment. In addition, the Fc-mediated nonspecific clearance pathway was not separately defined in the model since it was included as part of the total linear clearance.
For biologics binding to soluble targets, the ISF turnover rate in disease-associated tissues considerably influenced the magnitude and duration of target suppression. Tissue fluid turnover rates were thus systematically assessed as a critical factor for target suppression. As shown in our analysis, the tissue fluid turnover rate not only determines the tissue concentrations of antibody, but also affects the removal efficiency and accumulation of antibody-target complexes in disease-associated tissue. The latter limits the effect of biologics in suppression of soluble targets. On the other hand, for tissues with relatively fast fluid turnover, a ceiling effect was observed when decreasing dissociation rates beyond target tissue fluid turnover rates in seeking to increase binding affinity and further improve therapeutic efficacy. This concept is summarized in Fig. 5. For example, in the joint synovium, a tissue with fast fluid turnover, a fast binder (high kon) rather than a stable binder (low koff) is favored for improved target suppression. Target suppression cannot be increased when lowering koff beyond the fluid turnover rate. There is no numerical cutoff of kon and koff values for a given antibody to make it a faster or stable binder since it is dependent on the fluid turnover rates in the target tissues. Fast binders should be antibodies that have high kon values to allow the antibody and target to sufficiently bind during the fluid turnover process of their target tissues. Stable binders should be antibodies that have low koff values to keep the antibody-target complex during tissue fluid turnover. These findings have implications for prioritizing strategies in the development of biologics for soluble targets in tissues.
Optimization of target binding kinetics to achieve greater binding affinity is the first priority in developing biologics. However, as shown in our analysis, excessive pursuit of high binding affinity is not desirable and even wasteful (Rudnick et al., 2011); selection of sufficient binding affinity while particularly focusing on appropriate binding constants for a given diseased tissue is a rational approach. In surveying the 27 approved biologics with soluble targets, over 90% of the biologics treat diseases associated with tissues having fluid turnover rates higher than the system average. Three exceptions are bevacizumab, romosozumab, and eculizumab. Bevacizumab is an antibody targeting vascular endothelial growth factor, a proangiogenic cytokine used in treatment of multiple types of cancer (colon cancer, lung cancer, glioblastoma, and renal-cell carcinoma). In solid tumors that are generally believed to have low functional lymphatics, bevacizumab-vascular endothelial growth factor complexes are expected to accumulate with lesser therapeutic efficacy (Couzin-Frankel and Ogale, 2011; Van Cutsem et al., 2011; Khasraw et al., 2014). For tissues with relatively slow fluid turnover, strategies to increase lymph flow or enhance local degradation of the drug-target complexes might improve target suppression and efficacy.
Our mPBPK model was used to assess the influence of tissue fluid turnover rate on target suppression for biologics bound to soluble antigens. The simulations help explain why etanercept has limited efficacy in CD. For biologics that bind to soluble antigens in tissues, the interrelationships between target binding constants and tissue fluid turnover rates were also explored, showing that accumulation of drug-target complexes in tissues with slow fluid turnover can restrict the magnitude of target suppression. For tissues with high fluid turnover, a plateau effect was suggested for biologics with low target dissociation rates. These findings may help to prioritize strategies in the development of therapeutic antibodies for various diseases.
Acknowledgments
We thank Dr. Can Liu and Dr. Dongfen Yuan for providing assistance.
Authorship Contributions
Participated in research design: Li, Jusko, Cao.
Conducted experiments: Li, Cao.
Performed data analysis: Li, Cao.
Wrote or contributed to the writing of the manuscript: Li, Jusko, Cao.
Footnotes
- Received April 23, 2018.
- Accepted July 9, 2018.
This work was supported by the National Institutes of Health National Institute of General Medical Sciences [Grants R35 GM19661 and R01 GM24211].
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This article has supplemental material available at jpet.aspetjournals.org.
Abbreviations
- CD
- Crohn’s disease
- ISF
- interstitial fluid
- mAb
- monoclonal antibody
- mPBPK
- minimal physiologically based pharmacokinetic
- RA
- rheumatoid arthritis
- TNF-α
- tumor necrosis factor-α
- Copyright © 2018 by The American Society for Pharmacology and Experimental Therapeutics