Clearance in Drug Design - Journal of Medicinal Chemistry (ACS

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Cite This: J. Med. Chem. XXXX, XXX, XXX−XXX

Clearance in Drug Design Miniperspective Dennis A. Smith,† Kevin Beaumont,‡ Tristan S. Maurer,‡ and Li Di*,§ †

4 The Maltings, Walmer, Kent CT14 7AR, U.K. Pharmacokinetics, Dynamics and Metabolism, Pfizer Inc., Cambridge, Massachusetts 02139, United States § Pharmacokinetics, Dynamics and Metabolism, Pfizer Inc., Groton, Connecticut 06340, United States Downloaded via WESTERN UNIV on October 23, 2018 at 14:31:05 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

‡

ABSTRACT: Due to its implications for both dose level and frequency, clearance rate is one of the most important pharmacokinetic parameters to consider in the design of drug candidates. Clearance can be classified into three general categories, namely, metabolic transformation, renal excretion, and hepatobiliary excretion. Within each category, there are a host of biochemical and physiological mechanisms that ultimately determine the clearance rate. Physiochemical properties are often indicative of the rate-determining mechanism, with lipophilic molecules tending toward metabolism and hydrophilic, polar molecules tending toward passive or active excretion. Optimization of clearance requires recognition of the major clearance mechanisms and use of the most relevant in vitro and in vivo tools to develop structure−clearance relationships. The reliability of methods to detect and predict human clearance varies across mechanisms. While methods for metabolic and passive renal clearance have proven reasonably robust, there is a clear need for better tools to support the optimization of transporter-mediated clearance.

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INTRODUCTION

the typical exponential (log−linear) decline in drug concentration with time.

The importance of pharmacokinetics (PK) in the design of medicines has been well established.1−3 An effective drug needs to have an acceptable delivery route, sufficient access to the site of action, and a residence time sufficient for the desired duration of action. Modulation of the potential PK of a candidate series is a key aspect of the modern medicinal chemistry strategy. Previously in this series, the importance of half-life and volume of distribution in drug disposition has been discussed.4,5 This review will examine the basic theoretical and practical considerations relevant to rational drug design aspects of clearance.

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rate of elimination = CL × concentration

(1)

A typical plasma concentration versus time curve following intravenous (iv) administration is shown in Figure 1. This

DEFINITION OF CLEARANCE

Clearance is an important PK parameter to consider in both pharmaceutical research and clinical practice. It quantitates the irreversible removal of a drug from the measured matrix (typically blood or plasma). The key word in this definition is “irreversible”, which separates distribution into tissues (which is reversible) from clearance, where there is a permanent change to the molecule or removal from the body. The clearance parameter (CL) is one that links the measured concentration of the drug to the rate of elimination (eq 1). The units of CL are in volume per time, reflecting the volume of blood or plasma from which drug is completely eliminated per time. While CL is typically constant, the rate of drug elimination is concentration dependent. This is the basis for © XXXX American Chemical Society

Figure 1. Typical iv plasma concentration versus time curve for a compound in human.

exemplifies the information required to calculate clearance. By measurement of the concentration of drug in plasma over a time course, the area under the plasma concentration versus time course (AUC) can be calculated. This can be converted to clearance (CL) by relating to the dose through eq 2. CL = dose/AUC

(2)

Received: August 9, 2018 Published: October 3, 2018 A

DOI: 10.1021/acs.jmedchem.8b01263 J. Med. Chem. XXXX, XXX, XXX−XXX

Journal of Medicinal Chemistry

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relies on an understanding of oral bioavailability (F) and is explained by eq 4.

In this theoretical example, the dose was administered by the iv route. Under these circumstances, the entire dose has been placed into the systemic circulation, which is where the PK is measured. Consequently, the clearance calculated from this experiment is often quoted as systemic clearance (CLs). In addition, the matrix of analysis was (as often) plasma and so can also be quoted as plasma clearance (CLp). If blood were measured, then clearance would be blood clearance (CLb). The latter distinction is important since, depending upon the blood-to-plasma ratio (Rb) of a compound, clearance can be different when measured in plasma or blood. This reflects the ratio of concentration in whole blood (blood cells + plasma) versus that in plasma alone. It is not to be confused with the concentration in blood cells versus that in plasma. The relationship between CLb and CLp is given by eq 3. CL b = CLp/R b

CLpo = CLs/F

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(4)

IMPORTANCE OF CLEARANCE IN DRUG DISPOSITION Clearance is typically the most important PK parameter for a medicinal chemist to modulate in a chemical series. This is because clearance is a determinant of every other PK parameter of relevance in design, including half-life, oral bioavailability, and efficacious dose. (a) Effect of Clearance on Half-Life. Together with volume of distribution (Vd), clearance governs the elimination rate (kel) of a drug, and ultimately half-life (t1/2). Equations 5 and 6 provide a useful approximation of half-life from Vd and CL (which is exact in the case of a one compartmental PK model with linear clearance). From this relationship, a medicinal chemistry strategy that lowers systemic clearance within a series while maintaining volume of distribution should increase elimination phase half-life in a series.

(3)

As such, when Rb is unity (i.e., the drug is distributed evenly between the blood cells and plasma), the CLp equals CLb. Certain drugs, such as UK-224671,6 can specifically bind to components within the erythrocyte and drive to high Rb. In these cases, the concentration of drug in blood is significantly higher than in plasma and CLb is lower than CLp. Therefore, blood is a more appropriate matrix to measure clearance than plasma. In the case of UK-224671, 89% of the compound resides in the blood cell and blood clearance is significantly lower than plasma clearance. Conversely, many acidic molecules are significantly more highly bound in plasma than in the blood cells as blood cells do not expressed high concentrations of acid binding albumin. Therefore, even though the unbound drug will distribute into the blood cell (assuming passive membrane permeability), the acid will predominantly reside on the albumin, meaning that Rb values will collapse to the hematocrit (e.g., ∼0.6). In these cases, CLb can be up to 2-fold higher than CLp. The analytical matrix chosen to measure during PK experiments is an important consideration. Measurement of plasma clearly entails discarding of the blood cell fraction, which is appropriate if Rb approaches unity. When drug is extensively distributed into blood cells, then the majority of the drug in the blood sample will be discarded if plasma is the matrix of measurement. In these cases it is important to correct for Rb to account for CLb, since it is blood that circulates through organs, not plasma. If Rb is significantly greater than 1, measurement of blood rather than plasma should be considered. When R b approaches 1 or below, it is recommended to measure plasma and correct the CLp to CLb using Rb. The route of drug administration is critical for clearance assessment. As stated above, the iv route guarantees that all of the administered dose reaches the systemic circulation (or the blood compartment) where it can be measured. For all other routes of measurement, there are potential barriers that can prevent drug molecules from reaching the systemic circulation. This is the case for oral administration (po), where the dose is applied to the gastrointestinal (gi) tract and has to be absorbed and avoid first pass extraction by the gut wall and liver prior to reaching the systemic circulation. The AUC following po is often lower than that following iv of the same dose due to incomplete absorption, first-pass intestinal extraction, and/or hepatic extraction. The corollary to this is that clearance following po (CLpo) is most often higher than CLs. The relationship between oral clearance and systemic clearance

CL ≅ Vd × kel

(5)

t1/2 ≅ ln 2 × Vd /CL

(6)

(b) Effect of Clearance on Oral Bioavailability. Many small molecule drugs are administered by the oral route. If solubility and/or permeability is not optimal, absorption of the administered dose may be incomplete. During absorption, drugs are exposed to a host of metabolizing enzymes in the GI tract which can further extract a portion of the administered dose. Finally, the liver can also extract a portion of the administered dose by metabolism and/or transport due to the fact that drugs directly enter the liver via the hepatic portal vein before reaching systemic circulation. This process is termed hepatic first-pass extraction. If a drug is cleared from the blood by the liver, then the hepatic extraction is a key component of both oral bioavailability and clearance (eqs 7 and 8), where the terms are defined as F, fraction bioavailable; Fa, fraction absorbed; Fg, fraction escaping gut wall extraction; Eh, hepatic extraction ratio; and Q, hepatic blood flow. As clearance of a drug approaches hepatic blood flow, hepatic extraction ratio approaches 1 and oral bioavailability approaches zero. Consequently, minimizing hepatic extraction by lowering clearance is a key component in optimizing oral bioavailability. Other parameters, such as solubility, permeability, and transporter affinity, affect oral bioavailability. F = Fa × Fg × (1 − E h)

(7)

E h = CL/Q

(8)

(c) Effect of Clearance on Efficacious Dose. The importance of clearance in the relationship between steadystate concentration and dose was recognized early by Wagner in 1965 (eq 9),7 where Css,av is the average concentration at steady state (related to potency and target concentration) and τ is dose interval. Unlike the other determinant parameters, clearance optimization is expected to pay dividends in both the numerator (decreased CL) and denominator (increased F). Consequently, modulation of clearance has a direct effect on efficacious dose within a particular chemical series. B



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dose = Css,av × CL × τ /F

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aspect will be discussed in detail later. Overall, in order to improve dose via hepatic clearance within a chemical series, medicinal chemists should only focus on reducing CLint,u.

(9)

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FIRST CONCEPTS AND THE WELL-STIRRED MODEL In the evolution of PK, the concept of clearance was an important milestone. Borrowing heavily from engineering, a relatively simple and generally relevant mathematical model was developed to describe the removal of drugs from the body. A single organ of clearance such as the liver can be envisaged as having a flow of drug through which is defined by the blood flow to the organ (Q), the intrinsic ability of the organ to clear drug independent of flow or binding limitations (CLint,u), and the unbound fraction (f u) of drug available for clearance (eq 10).8−10 This is known as the well-stirred model of drug clearance. Although other mathematical models of clearance (e.g., parallel-tube and dispersion models) have been described, the well-stirred model has been shown to be broadly relevant and is perhaps the most commonly used and conceptually useful of the available models.11,12 CL = [Q × fu × CLint,u] /[Q + fu × CLint,u]

MECHANISMS OF CLEARANCE In order to be able to reduce CLint,u in a particular series, it is important to understand the mechanisms by which molecules within the series can be removed from the blood (Figure 2).

Figure 2. Simple model of clearance for an organ.

(10)

For an orally delivered and hepatically cleared small molecule, the well-stirred model equation (eq 10) can be combined with the dose and oral bioavailability equations (eqs 7 and 9, respectively) to produce a dose equation that neatly describes the fundamentals of clearance modulation in small molecule drug discovery (eq 11). dose =

The liver and the kidney are the two major organs of drug clearance. Passive and active processes, such as metabolism by enzymes and uptake/efflux by transporters, are the major mechanisms for drug clearance. Clearance by the Kidney. The major purpose of the kidney is to produce urine in order to excrete the byproducts of intermediary metabolism. Renal clearance is also a route for excretion of drugs and metabolites. Urine is produced by filtration of blood at the Bowman’s capsule (glomerular filtration) which allows any molecule with molecular weight of less than 50 kDa to pass through into the proximal convoluted tubule. Any useful products (such as glucose) are reabsorbed from the proximal convoluted tubule, and the balance of water and ions is modulated through the loop of Henle, before the urine passes through the ureter to the bladder. Any drug that is not bound to plasma protein will also be filtered into the proximal convoluted tubule. The rate of filtration is dependent on the flow to the glomeruli (rate of glomerular filtration, GFR ∼ 1−2 mL min−1 kg−1 in humans) and the unbound fraction of drug in blood (f u,b) as given by eq 12.

Css,av,u × CLint,u × τ Fa × Fg

(11)

This equation relates dose to steady state unbound average concentration (Css,av,u), unbound intrinsic clearance (CLint,u), dose interval (τ), fraction absorbed (Fa), and fraction escaping gut-wall extraction (Fg). In order to reduce the dose within a small molecule chemical series for orally administrated drugs, one can modulate one or a combination of the following properties: (1) Increase the potency against the pharmacological target (decrease target unbound concentration, Css,av,u). (2) Lower the rate of metabolism and/or uptake clearance by the liver (lower CLint,u). (3) Give the compound more often (lower τ). (4) Increase oral absorption (Fa) or fraction escaping intestinal extraction (Fg). For the purposes of this review on clearance, we have arrived at the fundamental parameter of unbound intrinsic clearance (CLint,u) that is the major driver for compound optimization in small molecule drug discovery. CLint,u is best described as the rate of removal of unbound drug by an organ (in this case the liver) in the absence of any blood flow or protein binding limitations. Equation 11 also points to two very important aspects of modulation of dose to improve drug disposition in a small molecule series. First, CLint,u is an unbound parameter, and so any assay designed to be used to investigate this parameter needs to correct for any binding in that experiment. Second, there is no factor in eq 11 for fraction unbound in plasma, despite its presence in the equations used in the derivation. Simply put, fraction unbound has no impact on unbound steady-state average concentrations and therefore is not expected, generally speaking, to impact dose requirements (i.e., protein binding is not a parameter to optimize13). This

CLr = fu,b × GFR

(12)

In accordance with Fick’s law, passive reabsorption of filtered drug is driven by permeability and the chemical potential created by the near complete (∼99%) reabsorption of filtrate over length of the nephron. As such, polar, poorly permeable drugs may be cleared at rates approximating the product of GFR and f u,b. At the other extreme, highly permeable drugs may be cleared at rates approximating 1% of the product of GFR and f u,b. Compounds can also be actively secreted to renal tubules by transporters (OATs, OCTs, P-gp) expressed at the proximal convoluted tubule that can remove drugs from the renal blood flow and deposit them into the urine for excretion. Depending upon the extent of reabsorption, this can result in CLr value exceeding the product of f u,b and GFR, with a theoretical upper limit approximating renal blood flow (RBF ∼ 16 mL min−1 kg−1 in humans). Clearance by the Liver. The liver is the major drug clearance organ. It is highly perfused receiving approximately C



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25% of the cardiac blood output. The architecture of liver allows for maximal mixing of the blood with the liver cells (hepatocytes). The hepatocytes possess an array of uptake/ efflux transporters (e.g., OATPs, P-gp, BCRP, and MRPs) and many drug metabolizing enzymes (e.g., CYPs and UGTs) that can promote hepato biliary secretion or metabolic clearance of a drug (Figure 3). Hepatobiliary clearance is generally an active

Figure 3. Schematic of the hepatobiliary system.

process, whereby a compound with relatively poor passive membrane permeability is recognized by a specific transporter on the sinusoidal (blood) side of the hepatocyte. Uptake of the drug into the hepatocyte is promoted by the transporter. The drug can then be actively secreted into the bile by bile canalicular transporters. By virtue of this mechanism, drugs can be rapidly cleared from blood and excreted in the bile. Alternatively, once a compound enters the hepatocyte (by active or passive processes) they can be subject to metabolism by CYPs, UDP-glucuronyl transferases, or one of the many drug metabolizing enzymes present in the hepatocyte. The metabolites formed by these enzymes can then be secreted into the bile (and excreted) or removed into the blood for excretion in urine. Due to the importance of the liver as a drug clearance organ, much work on optimizing in vitro assays using subcellular fractions has been completed. Liver obtained post-mortem from animals and humans can be treated in different ways to provide a variety of cellular and subcellular fractions that can be used to study metabolism and transport in vitro (Figure 4). A simple collagenase digest of liver tissue will liberate hepatocytes which can be cryopreserved or treated in a variety of ways to study drug metabolism and hepatic uptake. Homogenization of liver tissue in buffer followed by a slow speed (9000g) spin produces a pellet containing mitochondria and lysosomes. The supernatant (or S9 fraction) contains the majority of the phase 1 and phase 2 drug metabolizing enzymes of the hepatocytes (in a relatively dilute solution). A further high speed (100 000g) spin of the S9 fraction in the presence of calcium produces a supernatant containing all of the soluble enzymes present in the cytosol of the hepatocyte. The pellet contains the remnants of the cellular endoplasmic reticulum which in the presence of calcium have snapped back onto themselves to form spheres of membranes called microsomes. As such, microsomes do not occur in nature but are artifacts of hepatic subcellular fraction preparation. They have been a major driver of fully understanding hepatic metabolic clearance in vitro, since they contain all the membrane bound proteins, such as CYPs and UGTs. Hepatocytes and microsomes form the basis of two very useful in vitro reagents for study and prediction of metabolic intrinsic clearance. Ideally, study of the Michaelis−Menten

Figure 4. Preparation of hepatic subcellular fractions.

kinetics of any metabolic step would be completed at optimized enzyme and substrate concentrations. However, this would represent a considerably time-consuming experiment to complete for one compound let alone a series of compounds in a drug discovery project. To make this experiment more amenable for use in a drug discovery setting, some compromises have been made. The disappearance of a compound by metabolism in an in vitro incubation can be completed at a set enzyme and substrate concentration. When the substrate concentration is below the Km (Michaelis constant) for the metabolic enzyme, the disappearance will be a first-order process. The plot of substrate concentration versus time will be an exponential decline and a natural log transform will produce a straight line (Figure 5). The slope of this line is related to the Vmax/Km (Vmax, maximum reaction rate) of the molecule and can be transformed into the apparent intrinsic clearance (CLint,app) of that molecule accounting for cell number (for hepatocytes) or protein concentration (for microsomes) used in the incubation. (eq 13 and Figure 6). CLint,app =

0.693 mL incubation × t1/2 cell number or [protein]

(13)

This methodology has been extensively explained previously.14,15 Here it is important to remember that this is an apparent value that needs to be corrected for the unbound fraction in the in vitro incubation (f u,inc) in order to arrive at the unbound intrinsic clearance (CLint,u, eq 14). This is an important consideration because highly bound compounds may have a low CLint,app by virtue of a low f u,inc, leading to an underestimation of the clearance rate. Ultimately, CLint,u is the appropriate value to incorporate into the well-stirred model equation (eq 10) to predict the human hepatic clearance of that compound. CLint,u = CLint,app/fu,inc

(14)

The value of CLint,u at this point still has the units associated with the incubation (μL/min/mg protein for microsomes and μL/min/million cells for hepatocytes). In order to be useful for predicting in vivo CL, it needs to be scaled to the entire body D



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Figure 5. In vitro metabolism experiment following parent depletion in linear and log scales.

in vitro metabolic assays (such as microsomal and hepatocyte stability) became available.15,20 For the latter, there were a significant number of CYP substrates with human clearance values that validated the use of such assays in prediction of human clearance. The concepts of physicochemical properties (notably the positive correlation between lipophilicity and CLint,u) and availability of clinically validated in vitro metabolic clearance assays enabled optimization in drug discovery which now took into account metabolic intrinsic clearance in addition to target potency. There was a clear reduction in human PK attrition in the following decade.16 Subsequently, further scholarship on physicochemical properties driving toxicology led to an even greater emphasis on lipophilicity reduction.21 At the extreme, reductions in lipophilicity led to a reduction in membrane permeability and uncovered series of compounds where entry to the hepatocyte (either by passive or active processes) rather than metabolism is the rate-determining step in defining the clearance of these molecules. This is underlined in a comparison of clearance in hepatocytes and microsomes.22 As expected, compounds predominately metabolized by CYPmediated mechanisms gave comparable intrinsic clearance values in microsomes and hepatocytes when passive permeability was high. However, when hepatic passive permeability was low, intrinsic clearance in microsomes was faster than that in hepatocytes due to limited access to the enzymes within the cell. For certain non-CYP metabolism (e.g., aldehyde oxidase, reductases) that is not present microsomes, intrinsic clearance in hepatocytes will be higher. Some of the non-CYP enzymes have high extrahepatic expression, making it challenging to develop quantitative in vitro−in vivo correlation and accurately predict human PK due to lack of appropriate tools and species differences. Medicinal chemists need to pay attention to the impact of extrahepatic clearance in lead optimization. In addition, early observations indicated that application of the previously described scaling approach to CLint,u values derived from hepatocytes tended to underpredict the clearance of drug known to be substrates of uptake transport mechanism (e.g., OATPs) due to the lower activity of these transporters in hepatocyte preparations optimized for metabolic clearance estimation.23 These observations have led to an emphasis on the study of hepatic uptake as a potential rate-determining step in the clearance on molecules. The validation of in vitro assays for predicting hepatic uptake limited clearance has been limited by the relative paucity of compounds for which hepatic uptake is proven to be the rate-determining step in human clearance. Hepatocytes can be grown in culture under

Figure 6. Scaling human liver microsomal and hepatocyte CLint,app to human blood CLb.

by multiplying by the number of cells (or the microsomal protein concentration) per gram of liver and the number of grams of liver per kg body weight in the species of interest (Figure 6). This generates a predicted unbound intrinsic clearance value (units mL min−1 kg−1) which can be used in the well-stirred model to predict the CL of the molecule in that particular series. Clearly, the most important parameter to modulate in human in vitro metabolism experiments is the CLint,u as reducing this parameter will directly lead to improvements in requisite dose level and frequency. Herein lies the paradox with respect to clearance optimization in modern drug discovery. Medicinal chemists have become very adept at reducing metabolic intrinsic clearance within chemical series. This has led to a number of challenges, perhaps the most significant of which is a rise in the awareness and relative significance of other clearance mechanisms such as hepatobiliary transport.

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EVOLUTION OF LIVER CLEARANCE CONCEPT FROM METABOLISM TO ENZYME−TRANSPORTER INTERPLAY Decades ago in drug discovery, there was limited DMPK input into the optimization and selection of compounds for drug development. Compounds were largely optimized for potency against the pharmacological target. Since potency can (in part) be driven by lipophilicity, these early candidates tended to be large and significantly lipophilic. Such compounds tended to be good substrates for CYPs, and significant development candidate attrition was due to inappropriate PK (too short a half-life or too low oral bioavailability or poor exposure).16 Starting in the 1990s, significant scholarship on the effect of physicochemical properties on PK17−19 was completed and the E



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Table 1. Influence of Physicochemical Properties and Permeability on Factors Determining Drug Disposition Membrane permeability and physicochemical properties Absorption Access to target proteins

Low

Medium

High

Low log D < 0

Log D > 0 (PSA > 75 Å2)

Log D > 0 (PSA < 75 Å2)

Low unless MW < 250 Da and absorbed by paracellular route (atenolol) Extracellular including cell surface Rapid equilibrium with unbound drug in circulation Renal or biliary (possible transporter involvement)

Clearance

Variable. Influenced by permeability and transporters (nelfinavir)

High via transcellular route (propranolol)

Total body water but may be concentrated or effluxed from intracellular cytosol May have slower equilibrium with unbound drug in circulation Uptake and efflux transporters and metabolism

Total body water. Drug will show similar unbound concentrations to that present in plasma Rapid equilibrium with unbound drug in circulation Metabolism

Figure 7. Permeability controls the effects of transporter efflux or influx.

sequently directing researchers to the most appropriate systems by which to predict the composite clearance rate. The clearance parameters of the extended clearance model can be determined experimentally through a variety of approaches including the following: (a) PSinf,active and PSinf,passsive in suspension or plated human hepatocytes with and without uptake transporter inhibitors or at 4 °C; (b) CLint,met in human liver microsomal or hepatocyte incubations; (c) CLint,bile in human sandwich-cultured hepatocyte incubations (generally assuming that metabolism in this system is negligible). As shown previously, this methodology is based on a relatively new concept in the prediction of the human clearance of transporter-mediated uptake substrate both with and without further metabolism or biliary excretion (mixed clearance pathways). In contrast to purely metabolism rate limited clearance, the absolute scalability of parameters estimated in this way is still under investigation. More work is required to establish the scalability of these more complex approaches covering nonmetabolic or mixed mechanisms governing the rate of clearance.

conditions where they will express drug uptake transporters and potential form bile ducts (e.g., plated hepatocytes or sandwich culture hepatocytes).24−27 The intrinsic clearance values determined for uptake substrates tend to be much lower than would be required to explain the hepatic clearance of these compounds in vivo (for the few compounds where the full data set is available).28,29 Thus, extensive empirical scaling factors are currently required for prospective human clearance prediction.28,29 On a mechanistic level, the interplay between metabolism and transport is captured by what is called the extended clearance concept. This concept expands the hepatic CLint,u parameter into its component parts to provide a better understanding of the rate determining mechanisms of clearance (eq 15, which assumes all parameters represent the unbound values for the sake of simplicity). More specifically, in this concept, the rate determining mechanism of clearance is depicted as a complex function of passive clearance rate (PSinf,passive), active uptake rate (PSinf,active), metabolic intrinsic clearance (CLint,met), active sinusoidal clearance (PSeff,active), and biliary intrinsic clearance (CLint,bile). It is clear from this equation that the rate determining mechanism is not dependent upon the absolute value of any one rate but rather the rates of these individual mechanisms in relation to one another.

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hepatic CLint =

IMPORTANCE OF PHYSICOCHEMICAL PROPERTIES IN DETERMINING CLEARANCE MECHANISM On the basis of the above, providing the correct CLint,u data to estimate the clearance of a drug is highly enabled if physicochemical properties are considered. Permeability through lipid membranes determines not only the ability of a drug to be absorbed and reach an intracellular target, it also governs the fate of a molecule. Drugs with high permeability will be reabsorbed in the kidney tubule (following filtration at

(PSinf,active + PSinf,passive) × (CLint,met + CLint,bile) (PSeff,active + PSeff,passive + CLint,met + CLint,bile) (15)

Given the complexity of sorting the relative values of each process, classification systems that are based on experimentally obtainable data and/or physiochemical properties (e.g., ionization state) have been proposed as a means for predicting the likely rate-determining clearance mechanisms and conF



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For this reason, and the labor-intensive nature of generating multiple species PK profiles, some authors have suggested single species scaling with a fixed allometric exponent of approximately 0.7, as a pragmatic method for human clearance prediction of discovery compounds.37 However, it must be noted that fixing the allometric exponent is highly controversial.38 Small deviations in the exponent from the fixed value can lead to major mispredictions of human clearance, due to the large weight difference between some species (such as rodents) and human. The accuracy in prediction of human clearance by allometric scaling is dependent upon the clearance route and the extent of clearance. The best evaluation of allometric scaling prediction was completed by Tang et al.39,40 They examined 103 small molecule drugs with literature citations containing iv PK studies in at least three preclinical species and human. They found that errors in prediction of human clearance ranged from 0 (exact prediction) to greater than 3000% (major misprediction). Prediction accuracy was improved for high clearance molecules by metabolism and biliary secretion as well as molecules predominantly cleared by renal elimination. Predictability was poor for lower clearance molecules, especially where metabolism was a major player. This is not surprising since renal elimination is most likely by passive filtration at GFR, and according to the well-stirred model, high CLint,u compounds will tend toward hepatic blood flow clearance. Since blood flow scales well allometrically, compounds where clearance is dependent upon blood flow will predict well by this method. When extraction is toward the low level with respect to hepatic blood flow, clearance will be highly dependent on CLint,u. It is well-known that CLint,u values for a given compound in animals often are much higher than in human, and so allometric scaling for metabolized compounds is likely to overpredict the observed human clearance.41−43 The same is likely true for hepatic uptake cleared compounds as the drug transporters expressed in animals and humans are different.44 Overall, the use of animal PK experiments to predict human clearance is a useful approach. On a pragmatic level, if the clearance in animals is low with respect to liver blood flow in that species, it is likely that the human clearance will also be low. However, due to species differences in CLint,u, it is not unusual for clearance in animals to be high relative to liver blood flow and low in humans, and this approach may miss some important chemical series.

the Bowman’s capsule) and traverse the cell membranes at high intrinsic passive flux rates with minimal influence of transporters. Under these circumstances, clearance by the kidney can be discounted as such compounds will be cleared by metabolism. As permeability declines, so the rate of membrane flux declines and the transfer of the drug across lipid membranes is more impacted by transporters. Access to intracellular targets becomes problematic and the clearance fate of the molecule becomes more complex with potential for drug transporter involvement. Physicochemical measurements or calculations give good guidance into the likely permeability characteristics of a molecule. Low permeability is the result of low lipophilicity or high polar surface area (PSA). The role of physicochemical properties in determining many aspects of drug clearance and performance is shown in Table 1 and Figure 7. Many of these concepts have been incorporated into a variety of drug disposition classification systems,30,31 some of which (e.g., BDDCS, ECCS, ECCCS) seek to define the likely rate-limiting mechanisms of clearance (e.g., hepatic metabolism, hepatobiliary transport, renal excretion, or mixed mechanisms) from physicochemical properties (e.g., ionization state, molecular weight) and in vitro measures (e.g., membrane permeability, solubility, intrinsic clearance). Each has strengths and weaknesses with regard to theoretical and practical aspects as well as retrospective performance. While an extensive review of these classification systems is beyond the scope of this work,32 it is worth noting that the primary utility of these systems is to direct early discovery research efforts intended to characterize, predict, and optimize clearance of lead matter.

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ALLOMETRIC SCALING OF CLEARANCE Establishing in vitro to in vivo correlation in preclinical species for metabolic clearance and extended clearance is important to reduce uncertainty for human translation from in vitro to in vivo. In the absence of a simple in vitro assay to optimize and predict human clearance for hepatic uptake substrates, the fallback approach is often to use preclinical species PK scaling to predict human clearance. This practice has been known for many years and is termed allometric scaling. A true allometric scaling experiment requires iv administration to multiple preclinical species. The clearance of the compound (expressed as mL/min) is plotted against body weight (BWt) as shown in Figure 8. When plotted versus body

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PITFALLS IN ASSESSING CLEARANCE

From the above discussion, it is understood that optimization of small molecule candidates for acceptable human clearance is an essential part of the drug discovery process. It is also clear that clearance is a complicated property to assess, with many potential ways a compound can be cleared from blood and many potential species differences. Many oral drugs are required to be dosed once daily, leading to a requirement for high oral bioavailability and a half-life that matches the dose regimen. Consequently, the ability to assess and modulate human clearance within a series is essential. There are some potential pitfalls that can be encountered in assessing clearance in the drug discovery phase.

Figure 8. Basics of an allometric scaling experiment.

weight, the values for clearance in preclinical species can be extrapolated to human body weight to give a prediction of human clearance for that compound. It has been well-known for many years that physiological parameters (such as blood flow, heart rate, liver weight) scale with an exponent of less than 1 when plotted versus body weight.33−36 The same is often true of clearance (i.e., it is not a one to one relationship). G



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TIPS TO ADDRESS PITFALLS IN CLEARANCE ASSESSMENT (a) Understanding Extent of Binding of Molecules. As shown above, only compound that is unbound in an in vitro incubation is available to be metabolized or transported into hepatocytes to generate the true unbound intrinsic clearance (CLint,u). Compounds that are highly bound under the incubation conditions (binding to microsomes and hepatocytes) will tend toward low values for CLint,app but are very likely to be susceptible to rapid metabolism of the unbound drug due to high lipophilicity. Consequently, optimization of a series in the absence of understanding the extent of binding in the incubation will lead to apparent low in vitro intrinsic clearance that leads to high CLint,u in vivo. Similar issues apply to in vivo studies. PK studies exclusively measure total drug concentrations in blood or plasma, and CLs is a total parameter. However, only unbound drug is available to exert a pharmacological effect, and according to the wellstirred model, CLint,u is the major driver of exposure to unbound drug. Consequently, a high plasma protein binding (low f u) will affect CLs but will not affect CLint,u, meaning that it is possible to generate a low CLs with a highly plasma protein bound compound, but exposure to unbound drug will be low. In general, driving CLs lower by increasing plasma protein binding is not a good strategy, unless CLint,u is lowered or the compound has a high degree of pharmacological potency (i.e., low dose). The converse is also true. Lowering plasma protein binding in a highly bound chemical series does not improve exposure to unbound concentration, unless CLint,u is also lowered. Many groups have claimed to have improved drug properties by lowering plasma protein binding,45−47 but in all these cases the strategy to lower plasma protein binding was to lower lipophilicity with a consequent reduction in CLint,u. The effect of plasma protein binding on clearance and dose properties has been extensively studied,13,48,49 and in almost all circumstance, plasma protein binding modulation is not a viable method to improve a compound series. (b) Using the Correct in Vitro Assay. As noted above, there are numerous mechanisms that can drive the clearance of any particular chemical series (e.g., metabolism, uptake, renal). The in vitro assays that have been developed often separate these mechanisms such that one assay tends to focus on one particular mechanism. During the process of optimization of the clearance of a particular small molecule series, it is important to ensure that the in vitro assay used reflects the true mechanism of clearance of that series. If the assay does not have the appropriate mechanism, e.g., optimizing for metabolism when uptake is the issue or optimizing CYP when UGT is the main pathway, the translation to in vivo studies will be flawed. Also, in the process of compound optimization, if the structural changes made within a series drive to a different clearance pathway, then the in vitro assay used needs to reflect this. For example, the most likely response to high CLint due to metabolism is to lower lipophilicity in a chemical series. At some point, lowering lipophilicity will drive to poor membrane permeability and the rate-determining step could change to hepatic uptake or renal clearance rather than metabolism. Another issue with in vitro assays is often detection limits. Under certain circumstances even compounds that are cleared at the lower limit for the assay will scale to predicted in vivo clearance values that are higher than required for the optimal

dose regimen. Good examples of this are the in vitro assays for metabolic clearance. Hepatocyte and microsomal incubations are limited by the length of incubation time and ability to detect disappearance of the parent compound. Low clearance does not mean no clearance. A novel approach to increasing the incubation time for hepatocyte assays has been proposed as a method to lower the limit of determination and enable investigation of CLint values that are 10-fold lower than the standard methods used.50−52 (c) Focus on Low Dose. In progressing to in vivo PK studies, care must be taken to maintain as low a dose as possible. When clearance is an active process (such as metabolism or hepatic uptake), it will obey Michaelis−Menten principles. This means that clearance will only be dose- and concentration-independent at unbound concentrations that are significantly below the Km for the clearance mechanism. High dose studies (such as those used in toxicity studies) are not appropriate studies to generate PK parameters (such as clearance) as this requirement cannot be met and clearance mechanisms will often be saturated. (d) Use the Correct Terminology. Unfortunately the term clearance in its various forms is used incorrectly in many discussions. Drugs with a short half-life are often termed to be too rapidly cleared, whereas clearance may in fact be low, but the duration is compromised by a low volume of distribution. In other discussions, clearance is confused with recovery of the total dose (e.g.,14C labeled material) in urine and feces. Box 1. Key Messages (1) Understand the binding of a molecule both in vitro and in vivo, but do not optimize on binding. Rather optimize on unbound intrinsic clearance. (2) Make sure the enzymes that metabolize the compound series are present in the in vitro system used for optimization. (3) Pay attention to enzyme−transporter interplay and optimize compound series based on rate-determining mechanisms. (4) Maintain unbound concentrations below the Km of the enzymes/transporters clearing the series of molecules. Keep the dose/concentration relatively low. (5) Understand the (many) terms used to describe clearance.

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PERSPECTIVE Clearance is the most important PK parameter for drug disposition since it impacts half-life (or duration of action), oral bioavailability, and dose. It can only be calculated from inspection of the blood (or plasma) concentration versus time curve following iv administration. Measurement of blood (or plasma) means that clearance is a parameter based on total (both bound and unbound) drug. However, the unbound concentration of a drug is the driver of pharmacology and only unbound drug is available for clearance from the blood. Modulation of the human clearance within a small molecule chemical series is a key aspect of compound optimization during drug discovery. For clearance optimization, medicinal chemists need to focus on the unbound intrinsic clearance (CLint,u) which is effectively the maximal rate of clearance of drug by a clearing organ in the absence of flow and binding H



Perspective

limitations. The major organ of drug clearance in the body is the liver. Subcellular hepatic fractions have been used to prepare in vitro assays for metabolic clearance. These assays, along with understanding of the effect of physicochemical properties on clearance, have enabled drug discoverers to minimize metabolic CLint,u in chemical series and on the whole driven to molecules with lower clearance in human. However, by driving to lower lipophilicity, the rate of membrane penetration has potentially become a rate-determining step in clearance from the blood and the impact of transporters is more significant. With this trend, improved reagents, and an ever increasing understanding of the relevance of transporters in drug disposition, more compounds are being identified as drug transporter substrates and uptake has become a major player in clearance of small molecules. While this shift has been recognized in the thinking behind the extended clearance model, the in vitro assays required to lower CLint,u by uptake transport are still in their infancy. The next evolution of modulation of clearance in drug discovery will follow much greater experience in the application of these drug transporter assays.

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exclusivity. He also leads a modeling and simulation group responsible for both computational chemistry and quantitative translational pharmacology across Pfizer’s small molecule portfolio. Li Di has about 20 years of experience in the pharmaceutical industry including Pfizer, Wyeth, and Syntex. She is currently a Research Fellow at Medicine Design Department, Pfizer Global Research and Development, Groton, CT. Her research interests include the areas of drug metabolism, absorption, transporters, pharmacokinetics, blood− brain barrier, and drug−drug interactions. She has over 135 publications including two books and presented more than 85 invited lectures. She is a recipient of the Thomas Alva Edison Patent Award, the New Jersey Association for Biomedical Research Outstanding Woman in Science Award, the Wyeth President’s Award, Peer Award for Excellence and Publication Award.

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ABBREVIATIONS USED AUC, area under the curve; BCRP, breast cancer resistance protein; CL, clearance; CLb, blood clearance; CLint,app, apparent intrinsic clearance; CLint,bile, biliary intrinsic clearance; CLint,met, metabolic intrinsic clearance; CLint,u, unbound intrinsic clearance; CLp, plasma clearance; CLpo, oral clearance; CLr, renal clearance; CLs, systemic clearance; Css,av, average concentration at steady state; Css,av,u, unbound average concentration at steady state; CYP, cytochrome P450; DMPK, drug metabolism and pharmacokinetics; Eh, hepatic extraction ratio; F, bioavailability; Fa, fraction absorbed; Fg, fraction escaping gut wall extraction; f u, unbound fraction; f u,b, fraction unbound in blood; f u,inc, unbound fraction in in vitro incubation; GFR, glomerular filtration rate; GI, gastrointestinal; kel, elimination rate constant; Km, Michaelis constant; iv, intravenous; MRP, multidrug-resistance protein; OAT, organic anion transporter; OATP, organic anion transporting polypeptide

AUTHOR INFORMATION

Corresponding Author

*E-mail: Li.Di@Pfizer.Com. Phone: 860-715-6172. ORCID

Li Di: 0000-0001-6117-9022 Notes

The authors declare no competing financial interest. Biographies Dennis A. Smith worked in the pharmaceutical industry for 32 years after gaining his Ph.D. from the University of Manchester. During this period he directly helped in the discovery and development of eight marketed NCEs. More recently his roles include advisory boards or expert panels with many drug research based organizations, both industrial and academic, including Medicines for Malaria Venture and Cancer Research UK. His research interests and publications span all aspects of Drug Discovery and Development, particularly where drug metabolism knowledge can impact on the design of more efficacious and safer drugs.

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Clearance in Drug Design - Journal of Medicinal Chemistry (ACS

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