ADME Properties and Their Dependence on Physicochemical Properties

Abstract

Absorption, distribution, metabolism, and excretion (ADME) properties such as absorption, clearance, and volume of distribution are strongly influenced by physicochemical parameters. A lot of retrospective analyses have been performed to identify those attributes that give rise to favorable ADME parameters. These attributes should be incorporated in compound design to increase the chance of identifying a compound with superior ADME properties.

Contents

9.1 Abbreviations 165

9.2 Basic Concepts 166

9.3 Molecular Weight 170

9.4 pKa 171

9.5 Lipophilicity 173

9.6 Topological Polar Surface Area 175

9.7 Number of Hydrogen Bond Donors and Acceptors 175

9.8 Number of (Aromatic) Rings and % SP3 Carbon Atoms 176

9.9 Solubility 176

9.10 Multiparameter Optimization 178

References 180

Additional Reading 181

9.1 ABBREVIATIONS

ADME Absorption, distribution, metabolism, and excretion BCS Biopharmaceutics classification system CNS Central nervous system

S.C. Khojasteh et al., Drug Metabolism and Pharmacokinetics 165 Quick Guide, DOI 10.1007/978-1-4419-5629-3_9, © Springer Science+Business Media, LLC 2011

FaSSIF

Fasted state simulated intestinal fluid

FeSSIF

Fed state simulated intestinal fluid

HBA

Hydrogen bond acceptor

HBD

Hydrogen bond donor

iv

Intravenous

MV

Molecular volume

MW

Molecular weight

po

Oral

PSA

Polar surface area

RB

Rotatable bond

SA

Surface area

TPSA

Topological polar surface area

9.2 BASIC CONCEPTS

In vivo pharmacokinetic parameters, such as absorption, distribution, metabolism, and excretion are strongly influenced by the physi-cochemical properties of a drug. The earliest thorough analysis of ADME properties was performed by Lipinski and resulted in the famous "rule of 5", which argues that poor absorption is more likely if:

• The number of hydrogen bond donors (HBDs) >5 (counting the sum of all NH and OH groups)

• The number of hydrogen bond acceptors (HBAs) >10 (counting all N and O atoms)

Many analyses have been performed looking at the impact of physicochemical parameters on drug candidates. Although some companies do not pursue compounds that violate one or more components of the "rule of 5", the goal of these guidelines is not necessarily to rule out certain synthetic ideas. After all, several successful drugs violate the "rule of 5" to some extent: for example, atorvastatin, montelukast, and natural products such as cyclospor-ine and paclitaxel. These guidelines are more intended to steer the synthetic chemistry effort toward chemical space that is more likely to yield drugs with superior ADME properties. A brief summary of the physicochemical properties of marketed oral drugs relative to Lipinski's "rule of 5" is presented in Table 9.1.

Table 9.1. Average physicochemical properties of oral drugs in phase I and marketed oral drugs in comparison with Lipinski's "rule of 5"

Phase I

Marketed

Oral drugs

Oral drugs

Lipinski's

oral

oral

launched

launched

"rule

drugs

drugs

pre-1983

1983-2002

of 5"

MW (Da)

423

337

331

377

<500

clog P

2.6

2.5

2.3

2.5

<5

clog D7. 4

1.3

1.0

Number

2.5

2.1

1.8

1.8

<5

of HBDs

Number

6.4

4.9

3.0

3.7

<10

of HBAs

Number

7.8

5.9

5.0

6.4

of RBs

Number

2.6

2.9

of rings

Data from Wenlock et al. (2003) and Leeson and Davis (2004).

clog P = calculated lipophilicity when the compound is neutral; clog D74 =

calculated lipophilicity at a pH of 7.4; RB = rotatable bond

Data from Wenlock et al. (2003) and Leeson and Davis (2004).

clog P = calculated lipophilicity when the compound is neutral; clog D74 =

calculated lipophilicity at a pH of 7.4; RB = rotatable bond

Table 9.1 also highlights the fact that the average MW of oral drugs steadily decreases going from phase I to the market. This trend is also reflected in the number of HBDs and HBAs. (The same trend also applies when going from hit to lead to candidate in drug discovery.) However, the percentage of launched oral drugs that has a MW <350 Da has steadily decreased from 60-70% around 1985 to 30-40% around 2005 (Leeson and Springthorpe 2007), and there is a statistically significant difference between MW and the number of HBAs and rotatable bonds (RBs) between drugs launched before 1983 and between 1983 and 2002 (see Table 9.1). Note that nonoral drugs have different physicochemical properties. For example, injectable drugs have a higher number of HBDs, HBAs, and RBs and a higher average MW, as well as a lower mean clog P than oral drugs (Vieth et al. 2004).

Of course, ADME properties have to be balanced with other properties such as potency, selectivity, toxicity, etc. Indeed, significant differences exist in the average physicochemical properties of drugs as a function of the type of target such as ion channels, G protein-coupled receptors, proteases, kinases, etc. (Morphy 2006; Paolini et al. 2006). For some targets, such as those based on the disruption of protein-protein interaction, it is particularly unlikely to stay within the "rule of 5". In addition, the nature of the blood-brain barrier is such that cutoffs of certain physicochemical parameters, such as MW, number of HBAs, and RBs, and topological polar surface area (TPSA) need to be reduced for central nervous system (CNS) drugs. Physicochemical properties for oral CNS drugs are illustrated in Table 9.2.

Table 9.2. Average physicochemical properties of oral CNS drugs in phase I and marketed drugs in comparison with Lipinski's and Pajouhseh's rules for CNS compounds

Oral CNS

Oral CNS

Lipinski's

drugs

drugs

rule for

Pajouhseh's

launched

launched

CNS

rule for CNS

1983-2002

1983-2002

compounds

compounds

MW (Da)

310

377

<400

<450

clog P

2.5

2.5

<5

<5

clog D74

Number

1.5

1.8

<3

<3

of HBDs

Number

2.1

3.7

<7

<7

of HBAs

Number of

4.7

6.4

<8

RBs

Number

2.9

2.9

of rings

PSA (A2)

60-90

% PSA

16

21

pKa

7.5-10.5

Data from Leeson and Davis (2004) and Pajouhesh and Lenz (2005) PSA = polar surface area

Data from Leeson and Davis (2004) and Pajouhesh and Lenz (2005) PSA = polar surface area

It is important to keep in mind that the conclusions about correlations between physicochemical and ADME properties can be strongly influenced by the size and nature of the database employed. Moreover, many of the parameters are not independent of each other. Correlations between various parameters are illustrated in Table 9.3. For example, if MW increases, the number of RBs usually increases as well, and the TPSA and the total number of N and O atoms are also correlated (Vieth et al. 2004). Thus, it is possible that an observed correlation is merely fortuitous because other, more critical, properties have changed, which may actually be driving the correlation. Finally, some parameters may influence a range of ADME properties, while some may impact only one parameter. For example, the number of HBDs and HBAs has a pronounced impact on absorption but has a small impact on intestinal and hepatic extraction (Varma et al. 2010).

Table 9.3. Correlation coefficients between various physicochemical parameters

MW

clog P

ON

OHNH

Number of atoms

Number of rings

Number of RBs

Total SA

PSA

HBAs*

HBDs*

MW

0.18

0.45

0.12

0.96

0.51

0.50

0.88

0.33

0.39

0.13

clog P

-0.03

-0.55

-0.40

0.23

0.20

0.09

0.33

-0.60

-0.51

-0.38

ON

0.82

-0.44

0.43

0.41

0.04

0.36

0.28

0.93

0.79

0.42

OHNH

0.66

-0.44

0.78

0.11

-0.07

0.12

0.06

0.54

0.34

0.99

Number of atoms

0.97

0.01

0.82

0.65

0.59

0.49

0.92

0.28

0.32

0.12

Number of rings

0.55

0.20

0.34

0.21

0.62

-0.29

0.38

-0.06

0.07

-0.05

Number of RBs

0.77

-0.10

0.72

0.62

0.77

0.16

0.70

0.25

0.17

0.11

Total SA

0.96

0.05

0.78

0.64

0.98

0.54

0.84

0.14

0.18

0.07

PSA

0.74

-0.53

0.96

0.82

0.72

0.24

0.67

0.68

0.81

0.53

HBAs*

0.70

-0.46

0.87

0.64

0.67

0.26

0.54

0.62

0.88

0.32

HBDs*

0.66

-0.42

0.77

1.00

0.66

0.22

0.62

0.64

0.81

0.62

Correlation coefficients (r) for a dataset of 1,719 marketed drugs are displayed in the lower diagonal and r values for a subset that satisfies 10-90% MW coverage (196-563 Da) are displayed in the upper diagonal. Data from Vieth et al. (2004)

ON = number of oxygen and nitrogen atoms; OHNH = number of OH and NH groups; total SA = total surface area

*HBA is defined slightly different than ON, and HBD is defined slightly different than OHNH. See Vieth et al. (2004) for details.

The list of physicochemical parameters that influence ADME properties includes MW, pKa, log P, log D7.4, TPSA, % TPSA, HBAs, HBDs, (aromatic) rings, % sp3 carbon atoms, RBs, and solubility and we will focus on the correlation between these physicochemical parameters and their ADME attributes. A detailed explanation of the physicochemical underpinning of ADME properties is beyond the scope of this book.

9.3 MOLECULAR WEIGHT

MW can be calculated easily and is quite relevant from an ADME point of view. Elaborate studies have shown that permeability decreases with increasing MW; this observation fueled Lipinski to propose a cut off of 500 Da for potential drugs. However, some molecules with a MW >500 Da are absorbed. Many natural products have a MW >500 Da, but there are indications that absorption of some of these compounds may be mediated by uptake transporters. In some cases, MW is deliberately increased (and may exceed 500 Da) by making a prodrug to improve permeability. For example, olmesartan medoxomil is an ester prodrug and is absorbed well, whereas the active, dianionic drug is poorly absorbed (Fig. 9.1).

Olmesartan medoxomil Figure 9.1. Conversion of the prodrug olmesartan to the active drug.

MW is also loosely correlated with clearance whereby clearance increases with increasing MW. The reason may simply be that the number of metabolically reactive regions in molecules (also called "soft spots") increases with MW.

It has been argued that molecular volume (MV) is more relevant for the rate and extent of oral absorption and tissue distribution

Olmesartan medoxomil Figure 9.1. Conversion of the prodrug olmesartan to the active drug.

than MW (Lobell et al. 2006). MV is related to MW and can be obtained using (9.1).

However, halogen atoms should be accounted for differently because they have a relatively small volume for their atomic mass. To account for this effect, the following corrected atomic weights should be used for fluorine, chlorine, bromine, and iodine when calculating the corrected MW of halogen containing drugs: 5.2, 19.2, 26.3, and 37.4 Da (Lobell et al. 2006). (The real atomic weights of fluorine, chlorine, bromine, and iodine are 19.0, 35.5, 79.9, and 126.9 Da.) Many drugs contain multiple halogen atoms, such as fluorine and chlorine, to improve metabolic stability and/or the interaction with the target, but these drugs may have a MW >500 Da and, hence, violate Lipinski's "rule of 5". However, if the corrected atomic weights are used for the halogen atoms, the corrected MW may be reduced significantly and explain the favorable ADME properties. For example, amiodarone contains two iodine atoms and has a MW of 645 Da. However, the corrected molecular weight is 466 Da. (In addition, amiodarone has a log P of 8.9, but the log D74 is much lower at 3.4.) Indeed, the ADME properties of this compound are good: the bioavailability is 30% and the clearance is about 2 ml/min/kg (Chow 1996; Doggrell 2001) (Fig. 9.2).

Figure 9.2. Structure of amiodarone.

Figure 9.2. Structure of amiodarone.

Amiodarone

9.4 pKa

The degree of ionization of the drug is determined by the pH of the medium (acidic in the stomach and upper intestine, but close to 7.4 systemically) and the pKa. For acids:

For bases:

Knowing that pH = -log[H3O]+, (9.3) and (9.5) can be converted as shown below.

For acids:

For bases:

These equations indicate that, generally, acids (AH) will be neutral in the stomach, but the conjugated base (A-) will predominate in the intestine and blood. For bases, the acidic form (BH+) will predominate in the stomach, but basic compounds will be neutral in the intestine and blood, unless the compound has a pKa substantially greater than 7.4, as illustrated in Table 9.4.

Table 9.4. Ionization at pH = 7.4 for acids and bases as a function of pKa

Ionization at pH 7.4

Acid

Base

pKa

% ionized

pKa

% ioniz

4.4

99.9

5.4

1

5.4

99

6.4

10

6.4

90

7.4

50

7.4

50

8.4

90

8.4

10

9.4

99

9.4

1

10.4

99.9

The pKa value strongly influences solubility and drug absorption and distribution because it is assumed that only the neutral species can cross the lipophilic membrane. This phenomenon is illustrated in Fig. 9.3. In keeping with this phenomenon, acids tend to have a low volume of distribution (although this is also influenced by plasma protein binding) because the anionic state predominates in the plasma. In contrast, compounds with basic moieties, such as amines, tend to have a high volume of distribution. This is to a significant extent fueled by (1) sequestration in membranes (due to interaction with anionic phospholipids) or (2) trapping in acid organelles such as lysozomes.

AH +H2O

AH +H2O

cell membrane cell membrane A" + H3O+ BH+ +H2O :

Figure 9.3. Impact of ionization state on membrane permeability for an acid and a base.

9.5 LIPOPHILICITY

Lipophilicity reflects the affinity of a drug molecule to be associated with a nonpolar lipid-rich medium (in contrast to preferentially residing in an aqueous medium). Lipophilicity is measured by determining the partitioning between a buffered aqueous phase and an organic phase, usually n-octanol, and is expressed as either log P or log D. Log P reflects the partitioning when the compound is neutral (i.e., not charged), whereas log D is measured at a specific pH at which a fraction of the compound may be neutral and the rest positively or negatively charged depending on the pH and the pKa.

logP = log( [compoundorganic phase] / [compoundaqueous phase] ) (9.8)

logDpH = log ( [compoundorganic phase] / [compoundaqueous phase] )

Log P is included in Lipinski's "rule of 5" because at high log P values (>5), solubility is generally poor, which prevents absorption, and/or molecules partition into membranes and will not cross the enterocytes. Log P can be calculated easily (log P reflects the calculated value) and relatively accurately (usually ±1), but calculation of log DpH is more complicated, because it requires knowledge of the pKa. This explains why emphasis has been placed on determining log P. However, from a physiological perspective, log DpH is more relevant. For example, ebastine, a nonsedating H1 antihistamine, has a high log P of 6.9, but its log D74 is much lower at 4.6, because it is a basic amine and ebastine is partially charged at neutral pH. Log DpH is usually determined at pH = 6.0-7.4, which reflects the pH in the intestine (Fig. 9.4).

Ebastine

Figure 9.4. Structure of ebastine.

Ebastine

Figure 9.4. Structure of ebastine.

There appears to be a Gaussian or parabolic relationship between log D7.4 and the extent of absorption and bioavailability (see Fig. 9.5). At log D7.4 <0, a compound's solubility is good, but its permeability across membranes is poor, resulting in limited absorption. in addition, the metabolic clearance is usually limited, but clearance by the kidney may be high. In contrast, at log D7 4 >5, membrane permeability is adequate, but the solubility is low, which significantly reduces absorption. Moreover, metabolic clearance tends to be higher for compounds with log D7 4 >5. This effect is exacerbated by increased plasma protein binding (i.e., the unbound clearance is much greater at high log D7.4). However, this effect appears to be modulated by MW such that the log D range that leads to good absorption and increased metabolic stability is wider at low MW than at high MW (Johnson et al. 2009).

Plasma protein binding and tissue binding increases with increasing log P and log D7.4, and this can lead to a relatively low free drug concentration at the site of action despite reasonable membrane permeability.

Figure 9.5.

Relationship between lipophilicity and bioavailability/fraction absorbed.

Figure 9.5.

Relationship between lipophilicity and bioavailability/fraction absorbed.

poor solubility; poor metabolic stability

logP

Finally, there is a correlation between toxicity and clog P and TPSA with the observed odds for toxicity dramatically increasing with clog P >3 and TPSA <75 A2 (Price et al. 2009). This effect has been ascribed to increased off-target activity for nonpolar lipo-philic compounds (Leeson and Springthorpe 2007; Price et al. 2009).

9.6 TOPOLOGICAL POLAR SURFACE AREA

Calculating the exact polar surface area (3D PSA) can be time consuming because it involves calculating the three-dimensional structure and the PSA itself. The easiest and fastest approach is calculating the TPSA, which involves summing the contributions of individual polar fragments (Ertl et al. 2000). Although TPSA is based solely on the two-dimensional structure, the correlation between 3D PSA and TPSA was shown to be 0.99 for 34,810 molecules from the World Drug Index.

As described above, TPSA is an important parameter for passive membrane permeability, with a TPSA in excess of 120 A2 associated with poor absorption and a value in excess of 90 A2 associated with poor brain penetration. This effect can be ascribed to decreased membrane permeability for polar compounds. In contrast, a very low TPSA (<50 A2) can lead to increased intestinal and hepatic extraction (Varma et al. 2010), increased risk of off-target activity, and increased toxicity (vide supra; Price et al. 2009).

9.7 NUMBER OF HYDROGEN BOND DONORS AND ACCEPTORS

For the sake of ease of calculation, the number of HBDs and HBAs are usually equated with the sum of all NH and OH groups and the sum of all N and O atoms, respectively. The number of HBDs and HBAs is clearly correlated with the TPSA, and it has been established that the permeability and, hence, absorption, reduces with an increase in the number of HBDs and HBAs. On the basis of a database of 309 compounds with iv and po data in humans, Varma et al. (2010) showed that the fraction absorbed reduces significantly once the number of HBDs and HBAs exceeds 10, whereas the intestinal and hepatic extraction slightly decreases with increasing number of HBDs and HBAs. For CNS drugs, it is important to keep in mind that P-glycoprotein efflux increases dramatically with increasing number of HBDs (see Table 9.2).

9.8 NUMBER OF (AROMATIC) RINGS AND % SP3 CARBON ATOMS

The number of (aromatic) rings has a significant correlation with solubility, log P and plasma protein binding, and these parameters influence ADME attributes. For example, based on the analysis of a proprietary compound collection at GlaxoSmithKline, it was shown that 80% of the compounds with two aromatic rings have a clog P <3, whereas only 17% of the compounds with five aromatic rings have a clog P <3 (Ritchie and Macdonald 2009). The authors argued that compounds with more than three aromatic rings were associated with an increased risk of compound attrition and, therefore, should be avoided. However, the effect seems more pronounced for carboaromatic than metero aromatic rings (Ritchie et al. 2011) Similarly, others have shown a strong correlation between the fraction of sp3 atoms and solubility and melting point (Lovering et al. 2009). In addition, they showed that the % sp3 atoms steadily increases going from discovery to marketed drugs (36% versus 47%).

9.9 SOLUBILITY

Solubility is obviously of great importance for absorption of oral drugs. However, this parameter still remains somewhat elusive despite its apparent simplicity. Several aspects should be considered.

1. There are two types of solubility measurements: kinetic and thermodynamic solubility. The kinetic solubility is obtained by dissolving the compound in an organic solvent (e.g., DMSO) and adding it to aqueous buffer. Equilibrium is not reached between the dissolved compound and the solid, which may not be the most stable polymorph. Kinetic solubility measurements may be useful to assess the lack of solubility encountered in routine in vitro potency and ADME assays. However, kinetic solubility measurements are of little relevance to the situation encountered in vivo. Thermodynamic solubility is obtained by adding the aqueous buffer directly to solid crystalline material and waiting for an extended period of time for equilibrium between the dissolved and solid material. Although thermodynamic solubility is more relevant, it consumes more material and the measurement is time consuming.

2. The first synthetic batch is frequently amorphous, which usually results in increased solubility. Even if subsequent batches are crystalline, extensive experimentation may be required to identify the most stable polymorph, and the most stable polymorph is usually not identified until the compound has been nominated for development. Frequently, the most stable poly-morph will have a much higher melting point and reduced solubility.

3. Solubility is pH dependent. For example, moderately basic compounds may have very good solubility at pH = 1-2 (i.e., the situation encountered in the stomach), but the solubility may be much lower at the pH encountered in the intestine (see Fig. 9.6).

4. Solubility also depends on the matrix, and, therefore, thermo-dynamic solubility is usually measured in buffer, fasted state simulated intestinal fluid (FaSSIF; pH « 6.5), and fed state simulated intestinal fluid (FeSSIF; pH « 5). For example, felo-dipine is 100-fold more soluble in FeSSIF than in water and is not pH dependent in water.

5. It is possible to improve the dissolution of a drug by changing the salt form, reducing the particle size, or modifying the formulation (e.g., adding excipients to improve the solubility of lipophilic drugs). This enhances the solubility in the stomach and hopefully the compound stays in solution while it enters and moves along the intestine.

6. Not only is the extent of solubility important, but the rate should also be taken into consideration. The dissolution rate can be assessed in thermodynamic solubility experiments.

7. Solubility and dissolution rate are influenced by several physi-cochemical parameters listed above such as pKa, lipophilicity, etc.

8. A good computational model to calculate solubility reliably is still not available because solubility depends heavily on the crystal structure. Although it is possible to predict the crystal structure of small organic molecules (Day et al. 2009), such a prediction requires a massive amount of computing and is currently used only to predict polymorphs of compounds in development.

Poor solubility negatively affects absorption, in particular if permeability is poor to moderate. A key parameter to consider in this context is the size of the dose. In the biopharmaceutics classification system (BCS; see Chap. 3) a compound is considered highly soluble if the dose can be dissolved in 250 ml. The situation encountered in toxicology studies should be taken into consideration as well. It is possible that the solubility is compatible with absorption of the human dose, but it may not be possible to pH solubility profile of compound with pKa = 8.9

pH solubility profile of compound with pKa = 8.9

Figure 9.6. Solubility profile of a basic compound withpKa = 8.9.

Figure 9.6. Solubility profile of a basic compound withpKa = 8.9.

increase the exposure sufficiently to get the desired therapeutic index because of poor solubility.

9.10 MULTIPARAMETER OPTIMIZATION

Instead of looking at individual parameters, it is more advantageous to look at multiple parameters and, preferably, to weigh each parameter. This approach can be based on physicochemical parameters that can be calculated prior to synthesis, but it is also useful to incorporate measured parameters (such as enzyme and cell potency, metabolic stability in microsomes and hepatocytes, P450 inhibition, etc.) to facilitate identification of lead compounds and series in an objective fashion. A simple scoring system was proposed by scientists at Bayer and is depicted in Table 9.5 (Lobell et al. 2006). The final score is obtained by summation of the scores for each individual parameter with the best score being 0 and the worst score being 10. The authors showed that 70% of a database of 812 oral drugs had a score of 2 or less, whereas the average score of 13,775 confirmed high-throughput screening hits was 4.1. A more sophisticated approach has been advocated by Segall et al. (2009) who incorporated probabilistic scoring that allows parameters in the scoring profile to be given a different importance and it considers the uncertainty in each data point which is critical to distinguish molecules with confidence.

Although multiparameter optimization is empirical, two areas of controversy that continuously arise are (1) the weighing of the individual parameters and (2) the procedure applied to combine the parameters. The simplest and most common procedures for the latter are summation and multiplication. It is possible that a compound has reasonable scores in most areas, but a poor score in one particular area. If the individual scores are summed, a reasonable final score will be obtained for this compound despite the poor score in one possibly critical area. However, the final score will be small if the individual scores are multiplied.

Table 9.5. Simple scoring algorithm to categorize hits and leads

Solubility Score (mg/L)

1 10-50

clog P MWcorrected (Da)

3-5 400-500

Number PSA (A2) of RBs

Multiparameter optimization has also been explored for CNS drugs (Wager et al. 2010), because these drugs require a more narrowly defined chemical space. The individual scoring functions for six parameters, clog P, clog D, MW, TPSA, number of HBDs and pKa for CNS drugs are presented in Fig. 9.7. All parameters are weighted equally with the maximum value for each parameter being 1 and the final score being a summation of the value of all 6 parameters.

Figure 9.7. Example of scoring functions for clog P, clog D, molecular weight (MW), topological suface area (TPSA), hydrogen bond donors (HBDs) and pKa to enable multiparameter optimization for central nervous system (CNS) drugs.

References

Chow MS (1996) Intravenous amiodarone: pharmacology, pharmacokinetics, and clinical use. Ann Pharmacother 30:637-643 Day GM, Cooper TG, Cruz-Cabeza AJ et al (2009) Significant progress in predicting the crystal structures of small organic molecules - a report on the fourth blind test. Acta Cryst B65:107-125 Doggrell SA (2001) Amiodarone - waxed and waned and waxed again.

Expert Opin Pharacother 2:1877-1890 Ertl P, Rohde B, Selzer P (2000) Fast calculation of molecular polar surface area as sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem 43:3714-3717 Johnson TW, Dress KR, Edwards M (2009) Using the golden triangle to optimize clearance and oral absorption. Bioorg Med Chem Lett 19:5560-5564

Leeson PD, Davis AM (2004) Time-related differences in the physical property profiles of oral drugs. J Med Chem 47:6338-6348 Leeson PD, Springthorpe P (2007) The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov 6:881-890 Lobell M, Hendrix M, Hinzen B et al (2006) In silico ADMET traffic lights as a tool for the prioritization of HTS hits. ChemMedChem 1:1229-1236 Lovering F, Bikker J, Humblet C (2009) Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 52:6752-6756

Morphy R (2006) The influence of target family and functional activity on the physicochemical properties of pre-clinical compounds. J Med Chem 49:2969-2978

Pajouhesh H, Lenz GR (2005) Medicinal chemical properties of successful central nervous system drugs. NeuroRx 2:541-553 Paolini GV, Shapland RHB, Van Hoorn WP et al (2006) Global mapping of pharmacological space. Nat Biotechnol 24:805-815 Price DA, Blagg J, Jones L et al (2009) Physicochemical drug properties associated with in vivo toxicological outcomes: a review. Expert Opin Drug Metab Toxicol 5:921-931 Ritchie TJ, Macdonald SJF (2009) The impact of aromatic ring count on compound developability - are too many aromatic rings a liability in drug design? Drug Disc Today 14:1011-1020 Ritchie TJ, Macdonald SJF, Young RJ, Pickett SD (2011) The impact of arumatic ring count on compound develop ability further indiunts by examining carbo- and hetero-arumatic and -Aliphatic ring types Drug Disc today 16:164-171 Segall M, Champness E, Obrezanova C et al (2009) Beyond profiling: using

ADMET models to guide decisions. Chem Biodivers 6:2144-2151 Varma MVS, Obach RS, Rotter C et al (2010) Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. J Med Chem 53:1098-1108 Vieth N, Siegel MG, Higgs RE et al (2004) Characteristic physical properties and structural fragments of marketed oral drugs. J Med Chem 47:224-232

Wager TT, Hou X, Verhoest PR et al (2010) Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties. ACS Chem Neurosci 1:435-449 Wenlock MC, Austin RP, Barton P et al (2003) A comparison of physico-chemical property profiles of development and marketed oral drugs. J Med Chem 46:1250-1256

Additional Reading

Kerns EH, Di L (2008) Drug-like properties: concepts, structure design and methods: from ADME to toxicity optimization. Academic Press, Amsterdam, The Netherlands Mannhold R (2008) Molecular drug properties: measurement and prediction. Wiley-VCH, Weinheim, Germany Van De Waterbeemd H, Testa B (2008) Drug bioavailability: estimation of solubility, permeability, absorption and bioavailability, 2nd edn. Wiley VCH, Weinhein, Germany

Was this article helpful?

0 0

Responses

  • sandra
    How to improve ADME properties of a drug?
    7 months ago

Post a comment