Volume of Distribution in Drug Design - ACS Publications - American

Subscriber access provided by Northern Illinois University

Perspective

Volume of Distribution in Drug Design Dennis A. Smith, Kevin Beaumont, Tristan S Maurer, and Li Di J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.5b00201 • Publication Date (Web): 23 Mar 2015 Downloaded from http://pubs.acs.org on March 24, 2015

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Medicinal Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Medicinal Chemistry

Volume of Distribution in Drug Design

Dennis A. Smith1, Kevin Beaumont2, Tristan S. Maurer2, Li Di3*

1

2

4 The Maltings, Walmer, Kent, CT14 7AR, UK. UK

Pharmacokinetics, Dynamics and Metabolism, Pfizer Inc., Cambridge, MA, USA 3

Pharmacokinetics, Dynamics and Metabolism, Pfizer Inc., Groton, CT, USA

1 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abstract Volume of distribution is one of the most important pharmacokinetic properties of a drug candidate. It is a major determinant of half-life and dosing frequency of a drug. For a similar Log P, a basic molecule will tend to exhibit higher volume of distribution than a neutral molecule. Acids often exhibit low volumes of distribution. Although a design strategy against volume of distribution can be advantageous in achieving desirable dosing regimen, it must be well-directed in order to avoid detrimental effects to other important properties. Strategies to increase volume of distribution include adding lipophilicity and introducing basic functional groups in a way that does not increase metabolic clearance.

Key Words volume of distribution, plasma protein binding, tissue binding, half-life, lipophilicity, pKa


Page 2 of 37

Page 3 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Relevance of Volume of Distribution to Drug Design For most small molecules, clearance and volume of distribution have significant implications for duration of action through their relationship to half-life. Together with the rate and extent of absorption, these parameters represent key pharmacokinetic endpoints for the optimization of dose and dose regimen in lead development. High throughput screening assays have been widely applied to profile metabolic stability, permeability, solubility and transporters to guide structural modification and to develop structure-activity relationships (SAR); however, volume of distribution is not typically screened for, or the focus of design efforts, even though the tools are available. This is likely related to the fact that strategies for the optimization of molecules through modulation of volume of distribution (relative to other properties like clearance and absorption) are relatively unclear. Some of the most common questions which need to be answered in support of a rational approach are: (1) What is a “good” or “bad” volume of distribution? (2) Should structural modification be applied to improve volume of distribution or develop SAR? (3) How does one modulate volume of distribution without adversely affecting other properties, and (4) Does high volume of distribution translate to high activity in tissues? This perspective will address the key elements governing volume of distribution and how to use the information in drug design.

Definition of Volume of Distribution Volume of distribution represents the apparent volume into which a drug distributes based on the amount of drug administered and the concentrations subsequently measured in the plasma or blood. Although multiple volume terms are commonly used for various purposes, the volume of distribution at steady-state (Vss) is generally



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

the most calculated parameter as it is estimated by noncompartmental techniques. This value may be considered as the most appropriate value to consider in drug design, as it represents the apparent distribution volume associated with the steadystate dosing paradigm under which most drugs are developed. The value of other common volume parameters to drug design may be limited or difficult to estimate since they reflect apparent distribution volumes at specific points in the pharmacokinetic profile (Figure 1). For example, the volume of the central compartment (Vc) represents the apparent volume of distribution immediately following an intravenous bolus dose. This volume will typically be less than Vss and is generally thought to encompass highly perfused organs into which the drug initially distributes. Vc is widely used to calculate loading doses when it is desired that steady state be reached quickly, but it is not particularly amendable to optimization. Vβ represents another commonly used parameter which can be derived from the terminal elimination phase half-life. However, the utility of this pseudo-equilibrium volume of distribution is often limited by the fact in pre-clinical experiments, particularly in the drug discovery phase, lack sufficient sampling or analytical sensitivity to accurately determine this phase. The practical use of Vβ as a measure of volume of distribution is confounded by the fact that it is not a pure measure of distribution, but also a function of the clearance rate (Figure 1). As such, unlike that for Vss, changes in clearance will change the estimated value of Vβ even when the underlying distributional properties remain unchanged. Nevertheless, although Vβ provides a volume estimate greater than Vss, differences between Vβ and Vss are generally small for marketed drugs 1.


Page 4 of 37

Page 5 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The volume of distribution values for small molecule drugs should be compared to the physiological tissue volumes (Figure 2). Whilst these tissue volumes are not related to volumes of distribution of drugs, it is useful to compare drug distribution to such tissue volumes. For example, a compound with a Vss of less than total body water (0.6 L/kg) is considered to exhibit a low volume of distribution (Table 1). Those with Vss values between 0.6 and 5 L/kg are termed to be of moderate volume of distribution and above 5 L/kg are considered high. Finally, Vss is best determined following intravenous administration of drugs. Intravenous administration ensures that the entire dose reaches the systemic circulation and dose is a determinant of the Vss equation. Any other route of administration can result in loss of dose prior reaching the systemic circulation. Thus, a Vss calculated from these routes of administration would be modified by the bioavailability of the drug from that route. For example, a Vss calculated from an oral plasma concentration versus time curve would in fact be Vss/F, and would always be higher than the actual Vss, unless the oral bioavailability was 100%.

Physiological Properties Governing Volume of Distribution Physiological volumes and tissue partitioning Assuming distribution of unbound drug according to Fick’s law, Vss can be expressed according to the following conceptually useful equation which represents the physiological basis of volume of distribution2-4. ݂‫ݑ‬௣ ܸ‫ ݌ܸ = ݏݏ‬+ ෍ ቆ ቇ ܸ‫ݐ‬ ݂‫ݑ‬௧ As shown, Vss is a function of both the physiological volume of the plasma (Vp) and tissues (Vt). Tissues are considered to be any physiological compartment with a volume (e.g. red blood cells, organs), the sum of which contributes to the overall Vss 5 ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

as depicted. Steady-state tissue-to-plasma ratios are a function of the relative fraction unbound in plasma (fup) and tissue (fut) such that concentration ratios can be either much greater or less than 1. On a mechanistic level, the determinants of the overall unbound fraction in a given tissue can be both nonspecific (e.g. partitioning into tissue phospholipid) and specific (binding to proteins such as albumin or alpha-1 acid glycoprotein) in nature.

This view provides several physiological insights of general relevance to informing rational chemical design around Vss. First this fu-based partitioning into tissues is largely responsible for why Vss values can often exceed that of physiological volumes alone (i.e. plasma and tissue volume). Simply stated, most neutral and basic drugs bind to tissue constituents so have a low fut. As such, Vss can often be viewed as a ‘virtual’ volume, i.e., does not relate to known physiological volumes. This also provides intuition for why the Vss of acids, which tend to be highly bound in plasma (low fup) and have low affinity for tissue constituents (high fut) have low volumes of distribution. Finally, it is also clear why Vss is an inappropriate indicator of how well a drug distributes to tissues in a pharmacological or toxicological context as differences in Vss are primarily related to differences in protein binding and/or nonspecific partitioning in plasma and tissues. As such, wide ranges in Vss may have little-to-no implications for the relative unbound concentration of drug in tissues and plasma. The role of tissue and plasma binding will be expanded in the section “Physiochemical Properties Governing Volume of Distribution”

Other potential physiologic factors (pH gradients, electrochemical potential and active transport)


Page 6 of 37

Page 7 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The pH of intracellular fluid is typically slightly lower than that of the extracellular fluid (approximately 7.2 vs. 7.4). This drives a slight pH partitioning in accordance with the pKa of the drug and the Henderson-Hasselbach relationship. Likewise, mammalian cells maintain an electric potential (~ -40 mV) which can drive electrochemical partitioning in accordance with the degree of ionization and relative intrinsic permeabilities of the neutral and ionized species. Greater pH and electrochemical partitioning can occur in subcellular compartments where larger gradients exist (lysosome pH ~ 5, mitochondrial membrane potential ~ -170 mV). However, the impact of these effects to overall Vss can often be negated by the dominant relative effect of fup and the relatively small aqueous volumes of these spaces.

In addition to these other effects, many tissues express transporters that can lead to accumulation or exclusion of drug. For example, OATP transporters and P-gp are known to govern the distribution of a wide variety of drugs to the liver and brain respectively. As with pH and electrochemical partitioning, the effect of transporters in a given tissue to overall Vss can often be negligible as it is not only related to the degree of accumulation, but the relative physiological volume of that tissue and the other factors (e.g., binding to proteins and lipids) which tend to dominate overall volume of distribution.

Nevertheless, unlike tissue partitioning due to specific/non-specific binding, pH partitioning, electrochemical partitioning and active uptake will have implications for the concentration gradient of unbound drug. As such, the pharmacological and toxicological implications will depend upon the specific cellular site to which



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

accumulation occurs. Because of the potential insensitivity of Vss to these effects, local distributional effects are best evaluated by specific tissue and/or cell distribution studies based on determination of unbound drug concentration.

Physiochemical Properties Governing Volume of Distribution Although a variety of physiochemical properties relate to volume of distribution, acid/base character is the biggest overall determinant. Basic molecules tend to bind to α-1 acid glycoprotein and albumin in plasma with moderate to strong affinity in a manner related to lipophilicity. Perhaps most importantly, such molecules will tend to partition into phospholipid membranes due to interactions with negatively charged phospholipid head groups. The extent of membrane/tissue binding will also be dependent on the overall lipophilicity of the molecule. Significant advances have been made to predict drug tissue distribution using tissue composition-based models and in vitro methods 5, 6. Of the acidic phospholipids, phosphatidylcholine has the major influence in the binding of basic drugs 7. Concentrations of phosphatidylserine in tissues (lung > kidney > liver > muscle and heart > brain) reflect closely the ranking of the tissue partition coefficients (Kp) of many basic drugs which are ionized at physiological pH 8. Finally, bases can also accumulate to some degree by pH and electrochemical gradients within the cell outlined below7. As such, basic molecules will be generally drawn from the systemic circulation by these processes and will have volumes of distribution around 1 - 25 L/kg. Neutral molecules have no electrostatic interaction with phospholipid head groups and as such their extent of membrane/tissue binding will increase with increasing lipophilicity. Volumes of distribution will generally be of the order of 0.7 - 4 L/kg. At the other extreme, acids have low membrane affinity and very high affinity for albumin due to the lysine-rich


Page 8 of 37

Page 9 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


nature of this protein. Again lipophilicity is important, but very high plasma protein binding (due to the high concentration of albumin in plasma, ~ 650 µM) occurs for acids at lower lipophilicity than neutrals or bases. Extravascular water has considerable amounts of albumin present (~ 50% of that in plasma) and so acids may exhibit higher volumes than plasma or blood volume. However, due to the negative charge at physiological pH, acids will not bind significantly to the phospholipid head groups of membranes and so tissue binding will be low. In addition, pH and electrochemical gradients can also lead to a degree of distributional exclusion for acids as outlined above9. Based on the significant binding to albumin and limited binding in tissues, acids will generally have volumes of distribution between 0.1-0.4 L/kg, which is in accordance with the Vss equation above, i.e., fup/fut will collapse to fup (because fut ~ 1).

Table 2 provides the volume of distribution values for a number of drugs. All the drugs are of positive lipophilicity (Log P and Log D7.4) and of low polar surface area (PSA) indicative of high passive lipid permeability. The volume of distribution values range from low (3 L/kg) reflecting the above rules. As suggested above, these drugs show effectively identical unbound (free) drug concentrations throughout total body water (covering therefore plasma water, extracellular water, intracellular water, CSF, etc.) despite this wide range of Vss values. Thus all will interact with their target regardless of location, and in a manner governed solely by their pharmacology and unbound aqueous concentration (determined by dose and unbound intrinsic clearance). Indeed, the ability of unbound drug to distribute into tissues is unmasked by the Vss,u term (i.e., the Vss corrected for fraction unbound in plasma, Vss,u = Vss/fu). Based on this term, the acidic compounds



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

exhibit equivalent ability of unbound drug to partition into tissues to basic and neutral drugs (Table 2).

This is an extremely important point to understand when considering volume of distribution as small Vss does not mean an inability of the unbound drug to reach the site of action in the tissues. For example, the two CNS agents, haloperidol and trazadone, have unbound brain-to-unbound plasma concentrations of unity, despite having very different Vss values (18 vs. 0.6 L/kg) 10.

Major Determinants of Vss Modulation for Small Molecule Drugs (a) Changing Ionization State Has Profound Effect on Volume of Distribution Drugs shown in Table 2 are divided into three broad physicochemical classes (acids, neutrals and bases) and it can be inferred that moving between the drug classes will have profound effects on volume of distribution. If a chemical change such as introduction of a basic group into a neutral molecule can be accomplished without changes in potency, selectivity or unbound intrinsic clearance then this step can be an attractive strategy since aqueous solubility and volume of distribution (hence duration of action) should be improved.

There are several examples of broad changes and beneficial effects on volume of distribution and drug duration by the addition of a basic center. One is the discovery of amlodipine from nifedipine 11-13. Nifedipine is a lipophilic neutral molecule which drives to a moderate volume of distribution (Table 3). Similarly, nimodipine is a neutral molecule with a higher log P than nifedipine. Consequently, its volume of distribution is significantly higher than that of nifedipine. Both compounds exhibit


Page 10 of 37

Page 11 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


moderate to high clearance values leading to short elimination half-life and duration of action. Nifedipine is administered four times a day. In contrast, amlodipine, with an intermediate log P, exhibits a higher volume of distribution by virtue of its basic center (pKa 9.45). As a result, the elimination phase half-life is 34 hours and it is administered once a day. However, the changes may also bring undesirable effects such as loss of selectivity, particularly due to increased affinity for ion channels such as IKr.

Erythromycin is a macrolide antibiotic with one basic center (Figure 3). It exhibits a volume of distribution of 0.95 L/kg and a half-life of 2 hours 14. Introduction of a second basic center into the macrolide aglycone ring in erythromycin yields azithromycin. This change increases the volume of distribution to 33 L/kg and together with ~ 2 fold reduction in clearance results in a half-life of 69 hours15, 16. Azithromycin can therefore be given once a day for a short period compared to multiple administration per day for erythromycin over a prolonged period.

Another example been exemplified in a series of tetrahydropyran histamine type 3 receptor antagonists17. The receptor is tolerant of the addition of a second basic center. Pharmacokinetic relationships in rat were studied for this group of compounds in three distinct subseries. All compounds had a strong basic centre (pKa ~ 10) either as the sole basic centre or combined with a weak second base or a strong second base (Table 4). For a given intrinsic lipophilicity (LogP), the strong second base subseries showed higher volume of distribution than the weak second base subseries, which in turn exhibited higher values than the monobasic subseries. The half-life values were highly influenced by these values. Volume of distribution could be influenced by



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

modulating of the pKa of the second basic center and gave rise to compounds with the desired balance of pharmacokinetic properties (Table 4).

(b) Modulation of Lipophilicity has an Effect on Volume of Distribution If broad changes of physicochemistry can reap benefits in terms of Vss, is it possible to fine tune such changes in a rational drug design program for compounds in fixed broad physicochemical classes (acidic, neutral or basic)? The attractions of this are obvious as a hierarchy of stabilising a molecule to clearance processes and then being able to further refine the duration of action of a molecule by changes in tissue affinity would allow almost infinite variations. How this would be accomplished is not straightforward. Accompanying any ion-pair interaction described above is a strong influence of lipophilicity. Increasing lipophilicity may increase volume of distribution as exemplified in Table 3, but is also likely to increase unbound intrinsic clearance, resulting in no net gain in duration and a need to administer a higher dose (because clearance increased).

A strategy of blocking metabolic sites may introduce more lipophilic functions and thus have the dual outcome of reducing the unbound intrinsic clearance of a molecule and increasing its volume of distribution. A close correlation between lipophilicity, intrinsic clearance and volume of distribution has been shown for β adrenoceptor antagonists such that intrinsic clearance and volume of distribution increase proportionately with increasing lipophilicity and no resultant effect on half-life. However, betaxolol was discovered in a program to specifically reduce the unbound intrinsic clearance of metoprolol. The three-fold reduction in unbound intrinsic clearance produced by replacing methyl with the more metabolically stable methyl


Page 12 of 37

Page 13 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


cyclopropyl was accompanied by a three-fold increase in volume of distribution due to the increased lipophilicity. This combination increased the half-life to 17 hours relative to 4 hours for metoprolol (Table 5) 18.

This type of change would be very attractive but is difficult to achieve routinely. The cyclopropyl moiety should add 1-1.3 log units of lipophilicity but this change is almost unique. Typical strategies for stabilisation against oxidative metabolism involve halogen substitution. For a phenyl ring replacement of hydrogen by chlorine adds around 0.6 log units of lipophilicity, whereas fluorination only increases lipophilicity by 0.2 log units. Replacement of a benzylic methyl group (fragmental value around +0.5) with chlorine will make very little change and the same replacement with fluorine will decrease lipophilicity19.

Minor or Less Common Determinants of Vss Modulation for Small Molecule Drugs (a) Impact of Active Processes on Volume of Distribution The effects of active transport either into or out of an organ on Vss will only be significant if the transporters are widely expressed or expressed in major organs (in terms of % body weight). Moreover when expressed in major organs like the liver, to influence Vss there must be a component of redistribution of the drug back into the systemic circulation (otherwise it will be considered clearance).

Considering uptake transporters, a number of studies have examined the statins. Changes in pharmacokinetics and tissue distribution of pravastatin, atorvastatin and simvastatin were studied in wild type and oatp1a/1b-knockout mice lacking the three



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

major hepatic OATP isoforms and essentially devoid of hepatic OATP function 20, 21. Vss values in wild type animals were 0.27, 0.82 and 14 L/kg for pravastatin, atorvastatin and simvastatin respectively20. Relative to wild type controls, the effect of oatp1a/1b-knockout was to decrease clearance for pravastatin, atorvastatin and simvastatin respectively by 33, 75 and 42% and decrease Vss by 21, 60 and 14%. Based on the results of this study, OATP-mediated transporter impact on Vss appears to be minimal. Grove and Benet studied the transporter effects on volume of distribution of animals and humans using the published data from transportermediated drug-drug interactions, generic polymorphisms, or knockout animals22. The results showed that liver transporters have the most impact on volume of distribution followed by kidney. The magnitudes of the changes in volume of distribution are, in general, not very large and typically within 2-fold with some exceptions. With the complication of potential for increased clearance combined with minimal potential to increase Vss, a strategy to target tissue uptake transporters to increase Vss is not recommended.

(b) Enterohepatic Recycling Can Lead to Higher Apparent Volume of Distribution Enterohepatic recycling can occur when a compound is biliary eliminated as unchanged drug or metabolites, e.g., glucuronide23. Once released into the intestine the metabolites can be converted back to the unchanged drug, and they can be reabsorbed into the systemic circulation. Often the recirculation takes the form of “bumps” in the pharmacokinetic profile as the gall bladder empties and the effect can be identified24. The effect in rats, which lack a gall bladder, is more difficult to detect unless bile duct cannulation experiments are performed. In the bile duct cannulated rat, a typical drug could have a monophasic decline with high clearance. In contrast,


Page 14 of 37

Page 15 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


in the intact rat, the initial rapid phase seen in the bile duct cannulated rat is present, but now accompanied by a pronounced β phase giving a lower clearance and importantly a higher apparent Vss. An example of the change in half-life is provided by thienorphine which forms a biliary excreted glucuronide. The half –life of the drug in intact rats is 7 hours which shortens to 3.5 hours in bile duct cannulated animals 25. Reliance on this phenomenon as a strategy to increase Vss is fraught with difficulty and would not be recommended as a strategy to increase the half-life in a discovery series. Since biliary clearance is driven by hepatic uptake and efflux transporters which exhibit major species differences in expression, cross species extrapolation of the extent of enterohepatic recycling cannot be guaranteed. Also, increasing biliary elimination may well lead to an increase in clearance which could offset the improvement in apparent Vss. Nevertheless there are limited ways of obtaining long half-life compounds in acidic series, due to the low volume of distribution and it may be that most acidic compounds with half-lives greater than 12 hours in human rely on entero-hepatic recirculation to some extent. For instance, once a day nonsteroidal antiinflammatory agents (nonselective COX inhibitors) piroxicam, tenoxicam and benoxaprofen 26.

c) Target Mediated Drug Disposition When a therapeutic target has sufficiently high concentration in the body, binding to the target protein can increase the volume of distribution due to target-mediated drug disposition (TMDD). TMDD is frequently observed for biologics, where binding is highly specific to the target protein with exquisite potency 27. TMDD is relatively rare for small molecules. One example is the small molecule HSP90 inhibitors for treatment of cancers (Table 6)28. HSP90 is one of the most abundant intracellular



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

proteins in unstressed eukaryotic cells (1-2% of cytosolic protein). It has been shown that the more potent HSP90 inhibitors have larger Vss (~10 L/kg) than the less potent inhibitors (~2 L/kg). Fluorine substitution of hydrogen not only improved inhibition potency (Ki) by 3-5 fold, but also increased Vss by ~ 5 fold, while clearance remained relatively constant. The increase of Vss with enhanced target binding potency appears to be due to TMDD. This leads to sustained effect of the drugs on the tumours, because they are extensively distributed to the cancer tissues through high binding to the pharmacological target (HSP90). However, utilizing TMDD to enhance Vss and the duration of drug action of small molecules is likely to be opportunistic and rare. Furthermore, this would only work if the target is expressed at high enough levels to bind a significant portion of the drug that’s in the body.

Conclusions and Future Prospects Vss is one of the fundamental PK parameters of drug candidates. Even though its importance is often secondary to clearance in drug design, it plays a critical role in determining half-life and duration of action of a drug. SAR around Vss is typically not recommended, as properties that change Vss can also impact clearance, potency and selectivity leading to no net gain in in vivo performance. However, well-directed design and modification of Vss can result in longer or shorter duration of action as drug treatment requires. A large number of in silico, in vitro and in vivo tools are available to predict Vss with high confidence. Medicinal chemists will find these tools useful to help design the next generation of drugs with improved PK properties. The important take home messages on volume of distribution are highlighted below. •

Vss affects half-life and duration of action. For compounds with equal daily dose, the compound with lower Vss (shorter half-life) may need to be dosed


Page 16 of 37

Page 17 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


more frequently (at lower individual doses) to achieve a similar pharmacodynamic profile as the one with higher Vss. •

Large Vss does not indicate that drugs will reach the remote sites of action and be pharmacologically active. Similarly, low Vss does not mean that the compound will not reach the remote site of action.

•

High Vss and high total tissue concentration does not necessarily translate to better activity for disease targets in the tissues.

•

Transporters, pH gradients, electrochemical potentials can impact Vss, but the magnitude is usually small. Binding typically plays a dominant role in determining Vss.

•

Structure modification can be applied to increase/decrease Vss and duration of action, while maintaining clearance. Achieving this is not straight forward and requires well-directed strategies and design.

•

The major approaches to modulate Vss are introducing basic functional groups and increasing lipophilicity in a way that does not increase unbound intrinsic clearance.

•

Acidic compounds will need to have very low intrinsic clearance to have long duration of action, because their Vss are typically small.

•

Utilizing target-mediated drug disposition or exploiting entero-hepatic recirculation to increase Vss is opportunistic and relatively rare in drug discovery.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Acknowledgement The authors would like to thank Adam Gilbert for critical review of the manuscript, and Shinji Yamazaki for useful discussion.


Page 18 of 37

Page 19 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


* Corresponding author [email protected] 860-715-6172



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Biographical sketch: Dr. Dennis A. Smith worked in the pharmaceutical industry for 32 years after gaining his Ph.D. from the University of Manchester. Academic appointments include Honorary Professor at the University of Capetown. He is a member of a number of Expert Panels including ESAC with Medicines for Malaria Venture. 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. During this 38-year span he has helped in the Discovery and Development of eight marketed NCEs. He has co-authored over 160 publications including a number of books including “Pharmacokinetics and Metabolism in Drug Design”. He was recently elected as the first Fellow of the Drug Metabolism Discussion Group.

Mr. Kevin Beaumont has worked extensively in the discovery drug metabolism field, throughout his 30 years in the Pharmaceutical industry. His major area of expertise is in the modulation of physicochemistry to affect drug disposition and prediction of human pharmacokinetics. He is author on over 35 peer reviewed publications. Overall, Kevin has worked on many Discovery and Development projects throughout his career. He has been responsible for the DMPK input to at least 30 FIH studies as well as 10 Phase II compounds, including 1 marketed agent. Kevin now provides DMPK input to the Cardiovascular and Metabolic Disease Research Unit, based in Cambridge Massachusetts.

Dr. Tristan S. Maurer received his PharmD from the University of Georgia in 1993 and his PhD from the University of Buffalo, State University of New York in


Page 20 of 37

Page 21 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1999. During his 15 year tenure with Pfizer, his work has focused on the development and application of quantitatively rigorous, biologically-based methods to predict human pharmacokinetics & pharmacodynamics from preclinical data. He has co-authored over 50 manuscripts illustrating the utility of these methods to drug design and early clinical development. Currently, Dr. Maurer sits on the PDM leadership team responsible for scientific and operational strategy spanning from idea to loss of exclusivity. He also leads a modelling and simulation group responsible for both computational chemistry and quantitative translational pharmacology across Pfizer’s small molecule portfolio.

Dr. Li Di has about 20 years of experience in the pharmaceutical industry including Pfizer, Wyeth and Syntex. She is currently an associate research fellow at Pharmacokinetics, Dynamics and Metabolism 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 100 publications including two books and presented more than 70 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.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abbreviations AUC = Area under the plasma concentration versus time curve AUCu = Area under the unbound plasma concentration versus time curve Cav,u = Average unbound (or free) drug concentration CL = clearance CLb = blood clearance CLint = intrinsic clearance CLint, u = unbound intrinsic clearance F = Bioavailability Fa = fraction absorbed fup = fraction unbound in plasma fut = fraction unbound in tissue fuinc = fraction unbound under assay incubation condition IKr = the rapid component of the delayed rectifier potassium current kel = elimination rate constant Kp = tissue partition coefficient Log P or Log D = Lipophilicity OATP = organic anion transporting polypeptide PK = pharmacokinetics pKa = ionization constant PSA = polar surface area SAR = Structure-activity relationship τ = dosing interval t1/2 = half-life V1 = the initial dilution volume = Vc


Page 22 of 37

Page 23 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Varea or Vz = volume at the terminal phase of elimination = Vβ Vd = volume of distribution Vβ = volume of distribution at pseudodistribution equilibrium = Varea or Vz Vdc = volume of the central compartment = V1 Vss = volume of distribution at steady state Vd, u = volume of distribution of unbound drug



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

References 1.

Wagner, J. G. Significance of ratios of different volumes of distribution in

pharmacokinetics. Biopharm. Drug Dispos. 1983, 4, 263-270. 2.

Gillette, J. R. Factors affecting drug metabolism. Ann. N. Y. Acad. Sci. 1971,

179, 43-66. 3.

Oeie, S.; Tozer, T. N. Effect of altered plasma protein binding on apparent

volume of distribution. J. Pharm. Sci. 1979, 68, 1203-1205. 4.

Rowland, M.; Tozer, T. N. Clinical Pharmacokinetics and

Pharmacodynamics: Concepts and Applications. Fourth edition ed.; Lippincott Williams & Wilkins: 2010. 5.

Berry, L. M.; Roberts, J.; Be, X.; Zhao, Z.; Lin, M.-H. J. Prediction of Vss

from in vitro tissue-binding studies. Drug Metab. Dispos. 2010, 38, 115-121. 6.

Rodgers, T.; Rowland, M. Mechanistic Approaches to Volume of Distribution

Predictions: Understanding the Processes. Pharm. Res. 2007, 24, 918-933. 7.

Murakami, T.; Yumoto, R. Role of phosphatidylserine binding in tissue

distribution of amine-containing basic compounds. Expert Opin. Drug Metab. Toxicol. 2011, 7, 353-364. 8.

Yata, N.; Toyoda, T.; Murakami, T.; Nishiura, A.; Higashi, Y.

Phosphatidylserine as a determinant for the tissue distribution of weakly basic drugs in rats. Pharm. Res. 1990, 7, 1019-1025. 9.

Ghosh, A.; Scott, D. O.; Maurer, T. S. Towards a unified model of passive

drug permeation I: Origins of the unstirred water layer with applications to ionic permeation. Eur. J. Pharm. Sci. 2014, 52, 109-124.


Page 24 of 37

Page 25 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


10.

Maurer, T. S.; DeBartolo, D. B.; Tess, D. A.; Scott, D. O. Relationship

between exposure and nonspecific binding of thirty-three central nervous system drugs in mice. Drug Metab. Dispos. 2005, 33, 175-181. 11.

Faulkner, J. K.; McGibney, D.; Chasseaud, L. F.; Perry, J. L.; Taylor, I. W.

The pharmacokinetics of amlodipine in healthy volunteers after single intravenous and oral doses and after 14 repeated oral doses given once daily. Br. J. Clin. Pharmacol. 1986, 22, 21-25. 12.

Abernethy, D. R.; Schwartz, J. B. Pharmacokinetics of calcium antagonists

under development. Clin. Pharmacokinet. 1988, 15, 1-14. 13.

Hamann, S. R.; Piascik, M. T.; McAllister, R. G., Jr. Aspects of the clinical

pharmacology of nifedipine, a dihydropyridine calcium-entry antagonist. Biopharm. Drug Dispos. 1986, 7, 1-10. 14.

Welling, P. G.; Craig, W. A. Pharmacokinetics of intravenous erythromycin. J.

Pharm. Sci. 1978, 67, 1057-1059. 15.

Luke, D. R.; Foulds, G.; Cohen, S. F.; Levy, B. Safety, toleration, and

pharmacokinetics of intravenous azithromycin. Antimicrob. Agents Chemother. 1996, 40, 2577-2581. 16.

Foulds, G.; Shepard, R. M.; Johnson, R. B. The pharmacokinetics of

azithromycin in human serum and tissues. J. Antimicrob. Chemother. 1990, 25, 73-82. 17.

Hay, T.; Jones, R.; Beaumont, K.; Kemp, M. Modulation of the partition

coefficient between octanol and buffer at pH 7.4 and pKa to achieve the optimum balance of blood clearance and volume of distribution for a series of tetrahydropyran histamine type 3 receptor antagonists. Drug Metab. Dispos. 2009, 37, 1864-1870. 18.

Manoury, P. M.; Binet, J. L.; Rousseau, J.; Lefevre-Borg, F. M.; Cavero, I. G.

Synthesis of a series of compounds related to betaxolol, a new β1-adrenoceptor



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

antagonist with a pharmacological and pharmacokinetic profile optimized for the treatment of chronic cardiovascular diseases. J. Med. Chem. 1987, 30, 1003-1011. 19.

Manners, C. N.; Payling, D. W.; Smith, D. A. Distribution coefficient, a

convenient term for the relation of predictable physicochemical properties to metabolic processes. Xenobiotica 1988, 18, 331-350. 20.

Higgins, J. W.; Bao, J. Q.; Ke, A. B.; Manro, J. R.; Fallon, J. K.; Smith, P. C.;

Zamek-Gliszczynski, M. J. Utility of Oatp1a/1b-knockout and OATP1B1/3humanized mice in the study of OATP-mediated pharmacokinetics and tissue distribution: case studies with pravastatin, atorvastatin, simvastatin, and carboxydichlorofluorescein. Drug Metab. Dispos. 2014, 42, 182-192. 21.

van de Steeg, E.; Wagenaar, E.; van der Kruijssen, C. M. M.; Burggraaff, J. E.

C.; de Waart, D. R.; Oude Elferink, R. P. J.; Kenworthy, K. E.; Schinkel, A. H. Organic anion transporting polypeptide 1a/1b-knockout mice provide insights into hepatic handling of bilirubin, bile acids, and drugs. J. Clin. Invest. 2010, 120, 29422952. 22.

Grover, A.; Benet, L. Z. Effects of drug transporters on volume of distribution.

Aaps J. 2009, 11, 250-261. 23.

Roberts, M. S.; Magnusson, B. M.; Burczynski, F. J.; Weiss, M. Enterohepatic

circulation: physiological, pharmacokinetic and clinical implications. Clin. Pharmacokinet. 2002, 41, 751-790. 24.

Davies, N. M.; Takemoto, J. K.; Brocks, D. R.; Yanez, J. A. Multiple peaking

phenomena in pharmacokinetic disposition. Clin. Pharmacokinet. 2010, 49, 351-377. 25.

Deng, J.; Zhuang, X.; Shen, G.; Li, H.; Gong, Z. Biliary excretion and

enterohepatic circulation of thienorphine and its glucuronide conjugate in rats. Acta Pharm. Sin. B 2012, 2, 174-180.


Page 26 of 37

Page 27 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


26.

Benveniste, C.; Striberni, R.; Dayer, P. Indirect assessment of the

enterohepatic recirculation of piroxicam and tenoxicam. Eur. J. Clin. Pharmacol. 1990, 38, 547-9. 27.

Gibiansky, L.; Gibiansky, E. Target-mediated drug disposition model:

approximations, identifiability of model parameters and applications to the population pharmacokinetic-pharmacodynamic modeling of biologics. Expert Opin. Drug Metab. Toxicol. 2009, 5, 803-812. 28.

Yamazaki, S.; Shen, Z.; Jiang, Y.; Smith, B. J.; Vicini, P. Application of

target-mediated drug disposition model to small molecule heat shock protein 90 inhibitors. Drug Metab. Dispos. 2013, 41, 1285-1294. 29.

Obach, R. S.; Lombardo, F.; Waters, N. J. Trend analysis of a database of

intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab. Dispos. 2008, 36, 1385-1405. 30.

Hardman, J. G.; Limbird, L. E.; Gilman, A. G. Goodman & Gilman's The

Pharmacological Basis of Therapeutics, 10th Edition. 10th ed.; McGraw-Hill Professional 2001. 31.

Atkinson, A. J., Jr.; Bennett, J. E. Amphotericin B pharmacokinetics in

humans. Antimicrob Agents Chemother 1978, 13, 271-276. 32.

Kwon, Y. Handbook of Essential Pharmacokinetics, Pharmacodynamics, and

Drug Metabolism for Industrial Scientists. Kluwer Academic/Plenum Publishers: New York, 2001.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 37

Table 1. Classification of Steady State Volume of Distribution (Vss, L/kg) Species

Low

Moderate

High

Very High

All

< 0.6

0.6-5

5-100

> 100


Page 29 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2. Volume of distribution values for several acidic, neutral or basic drugs.

Drug

Ionization

pKa

Log P

Log D7.4

fu29, 30

PSA

Class

Vss

Vss,u

(L/kg)29, 31

(L/kg)

Indomethacin

Acid

3.9

4.2

0.7

68.5

0.01

0.096

9.6

Ketoprofen

Acid

4.2

2.9

0.2

54.4

0.008

0.13

16

Fluconazole

Neutral

--

0.5

0.5

71.8

0.89

0.75

0.84

Diazepam

Neutral

--

2.8

2.8

32.7

0.023

1.0

43

Chlorpheniramine

Base

9.1

3.4

1.5

16.2

0.056

10

178

Fluoxetine

Base

10.5

3.9

1.4

21.3

0.06

4.3

71

N N

N N

F

N

N

HO

O

N N

F

F

Cl F F

Indomethacin

Ketoprofen

Fluconazole

Diazepam

Chlorpheniramine

Fluoxetine

All of these molecules exhibit high permeability and can achieve unity in unbound concentration between the blood and tissues despite significant differences in volume of distribution. The volume of distribution values reflect differences in binding to proteins and membranes.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 37

Table 3. Physicochemical and pharmacokinetic parameters for two neutral and a basic dihydropyridine calcium channel antagonists 29.

Compound

Ionization pKa

Log P

Class Nifedipine

Log

Vss

CL

t1/2

D(7.4)

(L/kg)

(ml/min/kg)

(hours)

Neutral

--

2.9

--

0.79

7.3

1.9

Nimodipine Neutral

--

4.2

--

1.1

15

1.3

Amlodipine Base

9.45

3.3

1.2

17

7.0

34

Nifedipine

Nimodipine

Amlodipine


Page 31 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 4. Structures, pKa and Rat Pharmacokinetic Parameters of Monobasic (I), Strongly Dibasic (II) and Weak Second Base (III) Histamine Type 3 Receptor Antagonists 17

Compound

pKa 1

pKa 2

Vss

CLb

t1/2

(L/kg)

(ml/min/kg)

(hours)

I

10.0

--

6.5

240

0.6 L/Kg Plasma 0.04 L/Kg



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 37

Figure 3. Structure of Azithromycin and Erythromycin Erythromycin

Azithromycin

O

OH O OH

HO

O

O H

O

OH

O

HO O

O

O

HO

O

N

H OH O

O

N

O

N

OH OH

O O

OH


Page 37 of 37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure for Abstract

Blood pH 7.4 Proteins

Lipids Lysosomes pH 4.7 + 19 mV

Drug Mitochondria pH 8 -167 mV

Cytosol pH 7.2, -38 mV


Volume of Distribution in Drug Design - ACS Publications - American

Recommend Documents