Rational Use of Plasma Protein and Tissue Binding Data in Drug Design

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Rational Use of Plasma Protein and Tissue Binding Data in Drug Design Miniperspective Xingrong Liu,* Matthew Wright, and Cornelis E. C. A. Hop Genentech, Inc., South San Francisco, California 94080, United States ABSTRACT: It is a commonly accepted assumption that only unbound drug molecules are available to interact with their targets. Therefore, one of the objectives in drug design is to optimize the compound structure to increase in vivo unbound drug concentration. In this review, theoretical analyses and experimental observations are presented to illustrate that low plasma protein binding does not necessarily lead to high in vivo unbound plasma concentration. Similarly, low brain tissue binding does not lead to high in vivo unbound brain tissue concentration. Instead, low intrinsic clearance leads to high in vivo unbound plasma concentration, and low efflux transport activity at the blood−brain barrier leads to high unbound brain concentration. Plasma protein and brain tissue binding are very important parameters in understanding pharmacokinetics, pharmacodynamics, and toxicities of drugs, but these parameters should not be targeted for optimization in drug design.

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INTRODUCTION There is an increasingly frequent view in drug discovery that low plasma protein binding (PPB) is a good, “druglike” attribute, and some discovery programs go as far as to set a cutoff value for compound selection.1,2 In contrast to this common belief, we noticed that many clinically successful drugs exhibit high PPB. As for 260 marketed drugs approved by U.S. Food and Drug Administration (FDA) before 2003, almost 30% has PPB > 95%.3 This range is typically considered as high protein binding for drugs. Moreover, 5% has PPB > 99%, which is considered very high PPB. To further examine the trend of PPB for recently approved drugs, we compiled the available PPB data for drugs approved by the U.S. FDA from 2003 to 2013. Although the distribution pattern of PPB is similar to those of the previously marketed drugs, the recently approved drugs generally show even higher PPB than the previously marketed drugs (Figure 1). The PPB of 45% newly approved drugs is >95%, and the PPB of 24% is >99%. These data demonstrate that compounds with PPB > 99% can still be valuable drugs. Retrospectively, if we had posed an arbitrary cutoff value for the PPB in the drug discovery stage, we could have missed many valuable medicines in the past decade. We suggest that PPB is neither a good nor a bad property for a drug and should not be optimized in drug design. It is a commonly accepted hypothesis that unbound or free drug is the species available for interaction with pharmacological and toxicological targets in the body. This hypothesis is referred to as the free drug hypothesis in pharmacokinetics.4−8 Smith et al. further refine the expression of the free drug hypothesis to suggest that in the absence of transporters, the © 2014 American Chemical Society

Figure 1. PPB of 189 drugs approved by U.S. FDA from 2003 to 2013. Data are from the prescription information on each drug.

free drug concentration is the same on both sides of biological membrane at steady state and the free drug concentration at the site of action is the species that exerts pharmacological activity.9 For most organs there is no barrier between the blood and the organs to restrict the diffusion between them, and consequently the unbound drug concentration in the blood is the same as the unbound drug concentration at the drug target site. Exceptions to this are for those organs such as brain, in which there is an anatomical and biochemical barrier to restrict the exchange of drug molecules between the tissues and the blood. The unbound concentrations in these organs may not be equal to the unbound concentration in the blood. Received: May 22, 2014 Published: July 30, 2014 8238

dx.doi.org/10.1021/jm5007935 | J. Med. Chem. 2014, 57, 8238−8248

Journal of Medicinal Chemistry

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Acidic drugs mainly bind to albumin, and basic lipophilic drugs mainly bind to α1-acid glycoprotein and lipoproteins. If a drug has PPB of 99%, this means that 99% of the drug in the plasma is bound to the plasma proteins and 1% of the drug in the plasma is unbound. The 1% is defined as the plasma unbound fraction (f u) or plasma free fraction. The f u is often used in drug design, as it is a parameter to convert the total drug concentration, which is often measured experimentally, to the unbound concentration. For simplification, we assume blood and plasma unbound fractions are identical and they are used interchangeably. If they are different, a simple mathematical conversion can be applied to convert between the PPB and blood protein binding.10 It appears that one approach to increase the unbound concentration of a compound in the plasma is to optimize the compound structure to decrease its PPB based upon the definition of PPB. This apparently straightforward reasoning forms the notion that one can increase the unbound drug concentration by reducing the PPB in drug design. PPB has become a common assay in drug discovery organizations. Various low to high throughput methods have been developed. The main methods used in drug discovery are equilibrium dialysis and centrifugation in a 96-well format.11−14 The advancement of dialysis devices and mass spectrometry technology has allowed rapid generation of PPB data for many compounds, which subsequently enables the optimization of PPB in drug design. Moreover, various in silico models have been developed to predict PPB based on molecular properties, which have become new tools for medicinal chemists to design compounds with low PPB.2,15−18 In this review, we will discuss different aspects of PPB on unbound drug concentration in in vitro and in vivo circumstances. We will also discuss the impact of brain tissue binding on brain unbound drug concentration.

Figure 2. Unbound fraction of a compound in a medium ( f u,medium) versus the concentration of plasma proteins in the medium. The simulation was performed using eq 1 assuming plasma unbound fraction f u = 0.01 in 100% plasma.

the f u in human plasma. However, in cell based assays, the most commonly used plasma protein is fetal bovine serum. One may use eq 1 to estimate the f u,medium based on the observed human f u, but caution should be exercised for a potential error caused by species difference in PPB.20−23 Once a compound becomes a promising lead, the f u,medium should be determined experimentally. As an example to illustrate the impact of PPB on in vitro activities, Figure 3A shows the effects of human serum added into a cell medium on the cellular activation of peroxisome proliferator-activated receptor γ (PPAR-γ) by a partial agonist 1 (MBX-102, Figure 3).24 The relationship between activity and total drug concentration shifted rightward when the added human serum increased from 2% to 20% in the medium. The measured f u of 1 is 0.0090 in 100% human serum. Using eq 1, we estimated that the f u,medium values in the medium containing 2%, 10%, and 20% human serum are 0.31, 0.083, and 0.043, respectively. Using these calculated f u,medium, we generated Figure 3B from Figure 3A. The rightward shift disappears when the activity is plotted against the calculated unbound drug concentration in the medium. These results show that we should consider PPB in a cell based assay even though the medium may only contain a small amount of plasma proteins. It is common that potency in a cell based assay is worse than the potency in a protein based biochemical assay. This is often called shift of potency. The underlying mechanisms of the shift may include different PPB, cell membrane permeability, membrane drug transport, endogenous ligands, and/or ligand concentrations. If different compounds show different shifts, it is important to determine how much of the shift can simply be explained by the different PPB in the cell assay medium. One can use free cell potency, which can be calculated from the observed potency and the f u,medium in the assay, to remove the PPB effects. In one of our drug discovery programs, compounds 2 (GNE895) and 3 (GNE-242) (Figure 4) showed similar IC50 values (14 nM vs 19 nM) in an enzyme-based assay, where no plasma protein was added in the assay medium (red bars in Figure 4).25 But in a cell-based assay medium in which 10% fetal bovine serum was added, 2 and 3 showed IC50 values of 71 and 14 nM, respectively (blue bars in Figure 4). Why did 2 show a much weaker activity than 3 in the cell based assay? This can be explained by differences in their PPB in the cell assay medium. The f u of 2 and 3 are 0.014 and 0.10 in 100% fetal bovine serum, respectively. By use of eq 1, the calculated f u,medium in 10% fetal bovine serum is 0.12 for 2 and 0.52 for 3. These calculated values are consistent with the experimentally measured f u,medium of 0.16 for 2 and 0.57 for 3 in the 10%

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EFFECT OF PPB ON UNBOUND CONCENTRATION IN VITRO In many drug discovery programs such as kinase inhibitors and proteinase inhibitors, cell-based assays require 5−20% fetal bovine serum in the incubation medium to maintain the cell viability and functions during the assays. Although the concentration of the fetal bovine serum is low in the medium, a substantial amount of compound can still bind to the plasma proteins in the medium. The unbound fraction in a medium (f u,medium) can be estimated using eq 1: fu,medium =

1 (protein%)(1/fu − 1) + 1

(1)

where protein% is the percentage of the plasma or serum protein in the medium.19 Equation 1 is useful in the estimation of the PPB in the medium. Figure 2 illustrates that for high PPB compounds a significant amount of the drug can bind to the proteins in the medium. For example, if a compound has f u of 0.01 in fetal bovine serum, its f u,medium in the cell assay medium containing only 10% fetal bovine serum is approximate 0.1, meaning only 10% of the compound in the medium is free and available to interact with its target. Therefore, it is expected that the measured IC50 in the cellular assay for this compound could be 10-fold higher than the true IC50 or free IC50. For highly bound compounds, their f u,medium values are inversely proportional to the serum protein concentration, so the shift in moving to 100% serum could be 10-fold at worst. In a drug discovery setting, the most commonly measured PPB value is 8239



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Figure 4. IC50 values of 2 and 3 in enzyme based assay (red bar), cell based assay in a medium containing 10% fetal bovine serum (blue bar), and free cell IC50 (green bar).

these compounds are substrates for typical efflux transporters such as P-glycoprotein and if there is a correlation between efflux activities and the IC50 shift. In addition, we can also examine whether there is a concentration gradient across the cell plasma membrane. This gradient can be quantified using the ratio of unbound drug concentration in the cytosol to the unbound concentration in extracellular space.28,29 Recently, the cell and liver tissue homogenate approaches are proposed to estimate the unbound intracellular concentration for in vitro cell based assays.30,31 In this approach, the unbound fraction in cells is estimated from cell homogenate using eq 1. The total cell concentration following incubation with a compound solution is measured experimentally. Then the intracellular free concentration can be estimated from the total cell concentration and the cell unbound fraction.

Figure 3. In vitro activation of peroxisome proliferator-activated receptor γ (PPAR-γ) activity by a partial agonist 1 in a cell-based assay in the presence of 0%, 2%, 10%, or 20% of human serum. Parts A and B represent the activity versus the total concentration and the free concentration, respectively. The data for part A are from Clarke et al.24 The data for part B are from part A and the estimated unbound fraction using eq 1 with reported unbound fraction of 0.009 in 100% human serum.

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fetal bovine serum medium. By use of these measured f u,medium, the calculated free cellular IC50 values for 2 and 3 are 11 and 8 nM, respectively (green bars in Figure 4). These results demonstrate that the potency for these two compounds is similar after correcting for their PPB difference. Therefore, if the potency shift among compounds is due to different PPB in a cell assay medium, the difference in the potency shift should disappear when free IC50 values are considered. It has been shown that PPB can reduce the antiretroviral activity of lopinavir.26 The more plasma was added into the in vitro assay medium, the higher the apparent in vitro IC50 values were observed. However, after correction for the PPB in the incubation medium, the unbound IC50 remains essentially the same. Similar results were observed for other antiviral drugs.27 If different shifts still exist among compounds for their free cell IC50, then one may want to understand the mechanism of the shift and use this knowledge in drug design. Multiple factors may contribute to different shifts among the compounds. At the cellular level, membrane permeability and transport may contribute to the different shifts. For membrane permeability, we can examine the correlation between free IC50 shift and membrane permeability. For drug transport, we can examine if

IN VIVO EFFECTS OF PPB FOR A SINGLE COMPOUND Since most drug discovery programs are for oral drugs, we will mainly discuss the effects of PPB in the oral route of administration. It appears obvious that a decrease of PPB leads to an increase of unbound concentration based on the definition of PPB. This is true in vitro; however, the unbound concentration in vivo does not depend upon PPB after oral administration. The relationship between dose and plasma concentration, oral bioavailability (F), and total clearance (Cltotal) can be expressed in eq 2: F × dose AUCtotal = Cl total (2) AUCtotal is area under the total plasma concentration−time curve, which represents the overall drug exposure. For many drugs, their efficacy and toxicity are related to overall unbound plasma exposure or unbound AUC (AUCu). If we assume hepatic clearance is the major clearance mechanism, then Cltotal is determined by intrinsic clearance (Clin), PPB, and hepatic blood flow rate. The Clin represents 8240



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Figure 5. Effect of PPB on the hepatic clearance (A), bioavailability (B), total AUC (C), and unbound AUC (D). Simulations were based on the well-stirred model assuming complete absorption. The intrinsic clearance was set at 10%, 100%, and 10 000% of human hepatic blood flow rate (21 mL min−1 kg−1) for low, medium, and high clearance. Dose was set at 1 mg/kg, and dose interval was set at 24 h.

the liver’s maximal capability in elimination of the drug either through drug metabolism or through transport. The quantitative relationship between Cltotal and PPB can be described by various mathematical liver models.32−39 The most commonly used model is the well-stirred model. According to the well-stirred model, the hepatic clearance can be described by eq 3:33 Cl total =

AUCu =

(3)

Q is the hepatic blood flow and intrinsic clearance. Equation 3 shows that the lower is the PPB is (i.e., the higher the f u), the higher the Cltotal becomes (Figure 5A). This is an important concept in drug design, as we often consider that an increase of clearance is due to an increased metabolic rate instead of reduced PPB. This is particularly important when we compare the in vivo clearance for one compound to another when their PPB is much different. PPB also affects the oral bioavailability due to first-pass metabolism. To simplify this discussion, we assume no gut metabolism and complete absorption from the intestine. The oral bioavailability is determined by hepatic intrinsic clearance, PPB, and hepatic blood flow rate. According to the well-stirred model, the relationship between protein binding and oral bioavailability can be described by eq 4 assuming complete absorption: F=

Q Q + fu Cl in

(4)

Equation 4 shows that the lower the PPB is (i.e., the higher the f u), the lower the oral bioavailability becomes (Figure 5B). This concept is not well appreciated in drug design, as we normally associate low oral bioavailability with low solubility, permeability, or metabolic stability. We should be aware that low oral bioavailability could also be due to lower PPB. According to the well-stirred model, the AUCtotal and AUCu can be described by eqs 5 and 6, respectively: AUCtotal =

dose fu Cl in

(6)

Equation 5 indicates that an increase of the f u decreases total AUC (Figure 5C). Equation 6 contains no f u term, indicating that an increase of the f u does not change unbound AUC (Figure 5D). Conceptually, we can interpret eq 6 as that the gain of unbound concentration in vivo by an increase of f u is offset by an increase of the clearance and a decrease of the oral bioavailability. Thus, there is no net gain for the in vivo unbound drug concentration by just reducing PPB without reducing intrinsic clearance.40 One experimental method to study the relationship between PPB, total, and free plasma concentration is the isolated perfused rat liver model. In this model a rat liver is perfused in situ with a solution containing various amounts of plasma proteins.41 This experimental model can mimic in vivo oral administration by direct injection of a drug solution into the hepatic portal vein. It has been shown in this model that when the f u of propranolol increased from 0.10 to 0.62 in the perfusate containing different amount of plasma proteins, there is a reduction for the total concentration but no change for the unbound concentration just as predicted based on the wellstirred model.42,43 Similar results were reported for many other studies.42,44 Nagase analbuminemic rats have very low albumin in their plasma than the wild type rats.45 These animals are a good model to study the effect of PPB on the total and unbound drug concentrations in vivo. If a compound mainly binds to albumin in the plasma, its f u will be much higher in the analbuminemic rats compared to that in the wild type rats. It has been shown that the f u of clofibrate in the plasma from the analbuminemic rats was approximately 3-fold of that from the wild-type rats.46 After an oral dose, the total plasma concentration in the analbuminemic rats was about 1/3 of that in the wild type rats but the unbound plasma concentrations were similar between these two strains of rats.42 These results demonstrated that the 3-fold of difference in their PPB did not change their in vivo unbound concentrations in these two strains of rats.

Qfu Cl in Q + fu Cl in

dose Cl in

(5) 8241



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Figure 6. Effect of PPB on the total (A, C) and free (B, D) plasma concentration of compound 4 in CD rats and SD rats. Its unbound fraction in CD rat and SD rat plasma was 0.015 and 0.26, respectively. The compound was dosed intravenously (A, B) and orally (C, D). Data are from Ito et al.47

independent variables, meaning the PPB may change while the intrinsic clearance remains the same as shown in the theoretical analysis (Figure 5D) and experimental examples (Figure 6D) in the previous section. In the context of drug design, however, the PPB and intrinsic clearance are not independent variables. When we modify a compound structure to reduce its PPB, we change the physicochemical properties of the compound, which may lead to a change in its intrinsic clearance. There are limited examples in the literature that were designed specifically to examine this issue. Below we will discuss this issue with two selected examples. Rat pharmacokinetics of nine analogues of 5-n-alkyl-5-ethyl barbiturate derivatives was examined to investigate the effects of structure change on their pharmacokinetic parameters (Figure 7).50 The change of the alkyl group from methyl to nonyl for nine barbiturate derivatives resulted in f u changes from 0.01 to 1. For these nine compounds, an increase of their f u is associated with reduction of the intrinsic clearance (Figure 7A). A similar trend was observed for a much large data set (Figure 8A).51 Although only neutral compounds are presented in Figure 8, a similar relationship exists for acids, basic compounds, and zwitterions.42 In this large data set, if we examine a 1000-fold increase of f u ranging from 0.001 to 1, there appears to have a trend of a decrease of the intrinsic clearance. However, if we examine a range of 10-fold or less change of the f u, which often occurs in drug discovery setting, the association between the f u and the intrinsic clearance is no longer obvious. This suggests that in drug discovery, a few fold changes of f u may not change the intrinsic clearance accordingly. As an example from our drug discovery program, 2 and 3 are two close-in analogues. The f u of 2 is 1/15 of 3 in mouse plasma.

Another example to illustrate the in vivo effects of PPB on the total and free plasma concentration was from a rat pharmacokinetic study of 4 (D01-4582, Figure 6).47 Interestingly, its f u in the rat plasma from one strain (CD rats) was 1/18 of that from another strain (SD rats). The total plasma concentrations in CD rats were approximately 6-fold and 19fold greater than that in SD rats following an intravenous dose and an oral dose, respectively (Figures 6A and Figure 6C). However, their unbound concentrations were similar in these two strains of rats (Figures 6B and Figure 6D). These results demonstrated that 18-fold of difference in their PPB did not change their unbound concentrations in these rats. The theoretical simulations and in situ and in vivo experimental data illustrate that modulation of PPB without change of the intrinsic clearance does not alter the unbound concentration in vivo following oral administration. In addition, it is well-known in clinical studies that when PPB changes from patients to patients because of diseases or drug−drug interactions, the total plasma drug concentration can change but this seldom leads to a change of the free plasma concentration. Therefore, it is recommended not to adjust the dose in patients for oral drugs if the PPB changes are caused by disease or concomitant medicines.48,49

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IN VIVO EFFECTS OF PPB FOR A SERIES OF COMPOUNDS Relationship between PPB and Intrinsic Clearance. Most studies of the PPB effects have been trying to address the question: Does the unbound drug concentration change in patients when PPB changes due to diseases or concomitant medicines? In this context, the PPB and intrinsic clearance are 8242



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Figure 7. Correlation of intrinsic clearance (Clin), volume of distribution (Vdss), half-life (t1/2), plasma unbound fraction (f u), and log P. Data are from Blakey et al.50 The Clin was calculated using the well-stirred model assuming rat hepatic blood flow rate of 70 mL min−1 kg−1. The t1/2 was calculated from the clearance and Vdss using one compartment model. Reprinted by permission of Eureka Science Ltd. (Liu, X.; Chen, C.; Hop, C. E. Do we need to optimize plasma protein and tissue binding in drug discovery? Curr. Top Med. Chem. 2011, 11, 450−466).42 Copyright 2011 Eureka Science Ltd.

parameters differently. Figure 10 illustrates the relationship between the physicochemical properties that determine PPB versus intrinsic clearance including metabolic stability and drug transport activity. The blue circle represents the molecular properties determining the PPB, and the red circle represents the molecular properties determining the drug clearance. The overlapped space between the blue and red represents the physicochemical properties such as lipophilicity, which contributes to both the PPB and clearance. As the molecular properties governing PPB versus intrinsic clearance are only partially overlapped, reduction of PPB does not necessarily result in a reduction of the clearance especially when there is no change for the lipophilicity. It is clear that optimizing PPB without reducing intrinsic clearance will not increase in vivo unbound concentration. Therefore, one should focus on the reduction of the intrinsic clearance in drug design. There are several ways to discern if the intrinsic clearance is reduced in drug design. One approach is to determine intrinsic clearance from in vitro metabolic stability studies using human liver microsomes or hepatocytes. As many compounds may nonspecifically bind to the microsomes, hepatocytes, and even the assay apparatus, the observed clearance in the in vitro study may not be the truly intrinsic clearance. The intrinsic clearance may be estimated by correcting for those effects of nonspecific binding.

Following an intravenous dosing in mice, 2 shows approximately 2-fold higher intrinsic clearance than 3. After an oral dosing, the total AUC for 2 was 8-fold of 3 but unbound AUC of 2 was 1/2 of 3. These results indicate that a 15-fold increase in f u is associated with a 2-fold decrease of the intrinsic clearance. It is the 2-fold decrease of the intrinsic clearance leads to a 2-fold increase of the unbound oral AUC (Figure 9). The physiological function of PPB is to allow lipophilic endogenous substances such as steroid hormones to be carried away from their secretion sites to the rest of the body. Generally, high lipophilic compounds tend to have high PPB.18,51 For the nine barbiturates, high log P is associated with low f u (Figure 7B). A similar trend can also be observed in a large data set (Figure 8B). Likewise, lipophilicity also plays a critical role in determining clearance, especially for P450 mediated metabolic clearance. Figure 7D displays a trend between intrinsic clearance and log P for nine barbiturates, and Figure 8D shows a similar trend for a large data set. It has been demonstrated that lipophilicity is rather poorly correlated to albumin binding for a diverse set of molecules, but in congeneric series lipophilicity is often found to be the important factor, suggesting that specific molecular recognition elements are also essential for PPB.18 Clearly in addition to lipophilicity, other properties such as number of hydrogen bond donors and acceptors, pKa, and molecular shape also contribute to the PPB and clearance and may affect these two 8243



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Figure 8. Correlation of intrinsic clearance (Clin), volume of distribution (Vdss), half-life (t1/2), plasma unbound fraction (f u), and log P. Data are from Obach et al.51 The Clin was calculated from the well-stirred model assuming human hepatic blood flow rate of 21 mL min−1 kg−1. Compounds with clearance greater than 21 mL min−1 kg−1 were excluded from the calculation. The t1/2 was calculated from clearance and Vdss using one compartment model. Reprinted by permission of Eureka Science Ltd. (Liu, X.; Chen, C.; Hop, C. E. Do we need to optimize plasma protein and tissue binding in drug discovery? Curr. Top Med. Chem. 2011, 11, 450−466).42 Copyright 2011 Eureka Science Ltd.

Figure 9. Concentration−time profiles of 1 and 2 following an intravenous bolus dose (A, B) or oral dose (C, D). Total concentrations are shown in parts A and C, and unbound concentrations are shown in parts B and D.

Relationship between PPB and Volume of Distribution (Vdss) and Half-Life for a Series of Compounds. Vdss is a pharmacokinetic parameter that describes the apparent

extent of distribution of a drug in the body. This parameter is determined by the physiological volume of plasma, physiological volume of tissue, PPB, and tissue binding.52 In theory, if 8244



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is determined by the drug transporters at the BBB and several other factors, including diffusion clearance, uptake, and efflux transport clearance across the BBB, bulk flow, and metabolic clearance in the brain tissue.4,6,57,60 Equation 7 shows that unbound brain concentration is determined by unbound plasma concentration and the characteristics of the BBB, which is Kp,uu. It is important to note that there is no brain binding term in eq 7 and Kp,uu also does not depend upon brain binding. This means that brain tissue binding has no effect on the unbound brain concentration. Therefore, when we decrease brain tissue binding for a compound, we should not expect an increase of the unbound brain concentration. This view is supported by experimental data of 32 of the most prescribed CNS drugs.61,62 In that data set, the lowest and highest brain unbound fractions in brain were 0.00066 for sertraline and 0.76 for meprobamate. Both compounds are very successful drugs in the clinic, while their unbound brain fractions differ by 1152-fold! For 15 of 32 compounds, their brain unbound fraction is less than 0.05. There is no correlation between the brain tissue binding and the Kp,uu (Figure 11).

Figure 10. PPB and clearance are determined by different molecular properties. The blue circle represents the molecular properties governing the PPB. The red circle represents the molecular properties governing the clearance. The overlapped area represents the molecular properties such as lipophilicity contributing to both the PPB and clearance.

we can predict the tissue binding, we should be able to predict the Vdss. It has been showed that PPB and tissue binding are somewhat related and they tend to change in the same direction for neutral and basic compounds, as both lipophilicity and ionization contribute to tissue unbound fraction.53,54 However, it is still difficult to quantitatively predict the tissue binding from the PPB; consequently it is difficult to predict Vdss from PPB. For the nine analogues of barbiturates there is no clear correlation between Vdss and PPB as shown in Figure 7C.50 Similarly, no correlation was observed for a large data set (Figure 8C). Therefore, a reduction of PPB may or may not lead to a change of Vdss.51 Since t1/2 is determined by both clearance and Vdss and these two parameters are partially determined by PPB and tissue binding, it is more challenging to predict the effects of PPB on t1/2.3,55 Figure 7E shows that for the nine barbiturate analogues when the f u increased from 0.01 to 0.5, their t1/2 did not change significantly but when the f u increased from 0.5 to about 1, their t1/2 showed a trend of increase. However, this trend was not observed for a large data set (Figure 8E). These observations indicate that modulation of PPB for a compound may or may not change its t1/2.

Figure 11. Relationship between the brain tissue binding and the in vivo unbound brain/plasma concentration ratio (Kp,uu). Each circle represents one CNS drug. Data are from Maurer et al.61

Similar results were observed by other groups with multiple compounds from current CNS drug discovery programs and marketed CNS drugs.63,64 These results demonstrate that high brain tissue binding by itself is neither good nor bad for CNS drugs.

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SUMMARY PPB and brain tissue binding do not determine the unbound concentration in plasma and brain tissue, respectively. Therefore, it is not recommended to optimize PPB and brain tissue binding in drug design. High PPB or brain tissue binding per se should not be considered as a liability in selection of a drug candidate. The authors suggest measuring PPB and brain tissue binding in the following scenarios: 1. PPB should be determined to investigate if the potency shift is due to PPB. 2. PPB should be determined in pharmacokinetic and pharmacodynamic (PK/PD) studies so that the relationship between in vivo free concentrations and in vitro free IC50 for the pharmacological target or toxicological targets can be assessed. 3. PPB and brain tissue binding should be determined to calculate the unbound brain to unbound plasma concentration ratio in brain distribution assessment. 4. In human clearance prediction studies, binding in the microsomes and hepatocytes should be considered.

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EFFECTS OF BRAIN TISSUE BINDING ON UNBOUND BRAIN CONCENTRATION Brain is mainly protected by the blood−brain barrier (BBB). It is well-known that for those drugs targeted in the central nervous system, we need to optimize the compounds so they can cross the BBB. Once the drug molecules diffuse from the blood and reach to the brain tissue, they can bind to the cell plasma membrane, proteins inside the cells, and cellular organelles of the tissue. Questions have been raised whether we can increase the unbound tissue drug concentration by reducing the brain tissue binding.56−59 The in vivo unbound brain concentrations are governed by the following equation: Cu,brain = K p,uuCu,plasma (7) where Kp,uu is the ratio between unbound brain concentration and unbound plasma concentration at steady state. Kp,uu represents the efficiency for drug delivery across the BBB and 8245



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unbound brain concentration (μM); Cu,plasma, unbound plasma concentration (μM); F, oral bioavailability (%); f u, plasma unbound fraction; f u,medium, unbound fraction in a medium; Kp,uu, the ratio between unbound brain concentration and unbound blood concentration; PPB, plasma protein binding; Q, hepatic blood flow rate; t1/2, half-life (h); Vdss, steady-state volume of distribution (L/kg)

In drug design, we should place a stronger emphasis on improving the potency to reduce the targeted efficacious concentration and on reducing the intrinsic clearance to achieve high unbound plasma concentration. In addition, for drugs targeting in the central nervous system we should minimize drug efflux transport at the BBB to achieve high unbound brain concentration.

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REFERENCES

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*Phone: 650-467-4934. E-mail: [email protected]. Notes

The authors declare no competing financial interest. Biographies Xingrong Liu is currently a Senior Scientist in Small Molecule DMPK department at Genentech, Inc. He received his Ph.D. in Pharmacokinetics from the University of North Carolina at Chapel Hill. He worked on multiple drug discovery and development projects in neuroscience, inflammation, virology, ophthalmology, and oncology drug targets at Pfizer Groton, Roche Palo Alto, and Genentech laboratories in the past 16 years. His research interests include drug delivery across the blood−brain barrier and development of novel experimental and PK/PD modeling approaches in drug discovery and early clinical development. He has authored or coauthored more than 50 peer reviewed publications. Matthew Wright is currently Associate Director in Small Molecule DMPK at Genentech, Inc. with over 20 years’ experience in academia and industry. Matt received his Ph.D. in Biopharmaceutics from the Faculty of Pharmaceutical Sciences, University of British Columbia, Canada, and did postdoctoral training at the University of Alberta, Canada. Matt has extensive experience in supporting drug discovery and development programs at Gilead, Tularik (now part of Amgen), and DuPont Merck in a broad variety of therapeutic areas. Prior to joining the pharmaceutical industry, he was an Assistant Professor in the College of Pharmacy, Dalhousie University, Canada. Matt has experience in leading the DMPK/BA function to provide in vitro−in vivo ADME/PK-PD/bioanalytical support for programs spanning from initiation to market. Matt has authored or coauthored 60 peer reviewed publications. Cornelis E. C. A. Hop is Senior Director at Genentech Inc. and supervising the Small Molecule Drug Metabolism & Pharmacokinetics department. He leads a team of about 60 scientists involved in acquisition and interpretation of ADME data in support of drug discovery and development all the way to NDA filing and beyond. Before that he was a Senior Director at Pfizer (Groton, CT) and a Senior Research Fellow at Merck (Rahway, NJ). Dr. Hop has extensive experience in ADME sciences and biotransformation, PK prediction and bioanalysis in particular. He has authored more than 130 publications in refereed journals and several book chapters and has made more than 60 oral presentations at conferences and universities.

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ACKNOWLEDGMENTS The authors thank Guanming Chen for compiling PPB data for Figure 1 and Anthony Estrada and Mike Siu for their comments on this manuscript.

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ABBREVIATIONS USED AUCtotal, area under the total concentration−time curve (h·μM); AUCu, unbound area under the concentration−time curve (h·μM); BBB, blood−brain barrier; Cl, clearance (mL min−1 kg−1); Clin, intrinsic clearance (mL min−1 kg−1); Cu,brain, 8246



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Rational Use of Plasma Protein and Tissue Binding Data in Drug Design

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