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Acidic and Basic Drugs in Medicinal Chemistry: A Perspective
Paul S. Charifson*,† and W. Patrick Walters†
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Vertex Pharmaceuticals Incorporated, 50 Northern Avenue Boston, Massachusetts 02210, United States
ABSTRACT: The acid/base properties of a molecule are among the most
fundamental for drug action. However, they are often overlooked in a prospective
design manner unless it has been established that a certain ionization state (e.g.,
quaternary base or presence of a carboxylic acid) appears to be required for
activity. In medicinal chemistry optimization programs it is relatively common
to attenuate basicity to circumvent undesired effects such as lack of biological
selectivity or safety risks such as hERG or phospholipidosis. However, teams may
not prospectively explore a range of carefully chosen compound pKa values as
part of an overall chemistry strategy or design hypothesis. This review summarizes
the potential advantages and disadvantages of both acidic and basic drugs and
provides some new analyses based on recently available public data.
In many areas of medicinal chemistry it has been difficult to
separate experiential biases from an objective analysis of the
existing data. However, as greater amounts of data have become
available in a variety of areas associated with drug discovery, the
ability to re-evaluate some of these long held views has been
significantly enhanced. This is largely the incentive behind this
perspective, to perform a comprehensive review and analysis of
the properties and potential advantages and disadvantages
of acidic versus basic pharmacologic agents. While there are a
few reviews on this topic,1−4 other studies are included as part
of a broader property analysis.5,6 It is the goal of this work
to focus on the acidic and basic properties of small molecule
therapeutics and extend the analysis and key contributions of
previous work.
Any such work should start at the beginning, in this case
ancient Greece where the concept of acidity was originally
associated with the “sour tasting” aspect of acidic substances.7,8
The Greeks also observed that certain substances left over
from burning felt slippery to the touch, e.g., potash from wood
ashes and lime from burning seashells. As time went on, basic
substances were generally defined by their ability to counteract
the effect of acids. More formal attempts at classification of
acids and bases progressed through the contributions of
Lavoisier and Davy. However, it was not until the German
chemist von Liebig in the early to middle 19th century that the
concept of acidity was associated with the presence of
hydrogen. This was further refined by Arrhenius and eventually
led to the modern definition by Brønsted and Lowry wherein
an acid is defined as being a proton donor and a base as a
proton acceptor. In the early 20th century, Lewis further
extended the definition of acids and bases to include dissolution
events in nonaqueous solvents not involving free protons, i.e.,
Lewis acids as electron pair acceptors and Lewis bases as
electron pair donors. However, the definition of Brønsted
and Lowry is the most useful for discussions of ionic equilibria
© 2014 American Chemical Society
in aqueous systems and is the definition typically employed to
describe acidity and basicity as a property of drug substances.
It is fairly common for drugs to be classified as weak acids or
bases9 or perhaps more accurately as acids, bases, neutral, or
zwitterionic1,10 with approximately two-thirds of all existing
drug entities belonging to the class of weak electrolytes.10 As
such, many drugs have the potential to exist as ionic species
when dissolved in a variety of biological matrices. It has been
reported that most drugs are ionized in the range of 60−90% at
physiological pH.1,2 For the purposes of this review, we will use
the terms acids and weak acids, as well as bases and weak bases
interchangeably. Estimates from this work evaluating all drugs
in the ChEMBL database, and assuming at least 50% ionization,
suggest that this value is closer to the lower end of this range,
i.e., 60% ionization at pH 7.4. Figure 1 shows an analysis
of percent ionization over a physiologically relevant pH range
for 661 drugs containing a single ionizable group from the
ChEMBL-18 database. This data set comprises 237 acids and
424 bases spanning a pKa range from 2 to 10. It is apparent
from this analysis that the percent ionization of acidic compounds suggests complete ionization at pH values greater than
7.0 while for basic compounds the same trend holds at pH
values less than 7.0. While this result is expected, it also demonstrates that there are a significant number of both acidic and
basic drugs that possess a range of ionization states. Superimposed on this plot are the pH values of a variety of human
tissues and cell compartments. Most human tissues are quite
close to neutral pH with gastric and duodenal pH being the
predominant outliers.12 In general, basic compounds are poorly
absorbed from the stomach, since they are predominantly in
the ionized form in this low pH environment (pH 1.4−2.1, fasted
state). However, some weakly acidic and neutral drugs can
theoretically be absorbed from the stomach,12 although clear
examples of gastric absorption only (in the absence of intestinal
Received: July 3, 2014
Published: September 2, 2014
dx.doi.org/10.1021/jm501000a | J. Med. Chem. 2014, 57, 9701−9717
Journal of Medicinal Chemistry
Figure 1. Distribution (Beeswarm plot) of degree of ionization across a pH range for ChEMBL drugs with a single ionizable group. Acids are colored
red, and bases are colored blue.
absorption as well) are quite rare. Most of the cellular compartments that deviate from neutral pH tend to be more acidic, and
this can impact cellular and tissue distribution.13
It is well established that the degree of ionization impacts
several key molecular properties including permeability,
lipophilicity (partition or distribution coefficient), and
solubility.1−6 Perhaps the most common example of this
involves modification of pKa leading to an increase in polarity,
resulting in the same net impact as lowering lipophilicity.1,2,11,14
According to pH-partition theory, it is the un-ionized form of
substances that preferentially traverses gastrointestinal and
other lipid membranes by passive diffusion.12 This simplified
model can be represented by the Henderson−Hasselbach
equation to determine the relative amount of ionized species
based on the pKa of the compound and the pH of the environment. For a weak acid, this relationship is
pH − pK a = log
Figure 2. Analysis of different % ionization thresholds to define acids
and bases. Data are based on a set of 967 drugs from the DrugBank
determined experimentally or computationally, log D is usually evaluated at physiological pH, 7.4 with octanol as the
organic phase, although other organic phases have been used
Conversely, for a weak base:
pH − pK a = log
⎛ [solute]
log Poct/wat = log⎜⎜
un‐ionized ⎟
⎝ [solute]water
While pH-partition theory describes the vast majority of
cases, it should be emphasized that this is not an absolute; i.e., a
small amount of ionized species can permeate membranes
passively,1,12,15,16 including zwitterions.5,17,18 It has been
suggested that zwitterionic fluoroquinoline antibacterial agents
passively cross membranes in antiparallel stacked arrangements
that reduce overall electrostatic potential and polarity, thus
presenting themselves to membrane bilayers as neutral
Another key point to note is that pH-partition theory ties
lipophilicity to the rate and extent of compound absorption/
membrane permeability. This concept provides the basis for
extension of the partition coefficient, log P, to include the
relative amounts of both the ionized and neutral (un-ionized)
species into the distribution coefficient, log D. Whether
log Doct/wat = log⎜⎜
un‐ionized ⎟
⎝ [solute]water + [solute]water
Since octanol may also contain a small amount of water,
some degree of ionization can occur in the organic phase as
well. Thus, log D formally represents the sum of all ionized
species in both phases.
An additional point to be made in this discussion is that
there is some evidence suggesting that at least some, if not
most, small molecules may get into cells through carriermediated uptake.82 In such cases, the acidity or basicity of a
given compound might impact absorption/permeability more
through carrier selectivity rather than the overall extent of
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Figure 3. Distribution of acidic, basic, neutral, and zwitterionic compounds across the four most highly populated target classes from ChEMBL 18.
Two different activity cutoffs (100 nM and 5 μM) are considered.
Figure 4. Distribution of acidic, basic, neutral, and zwitterionic drugs by therapeutic area. Data are based on a set of 967 drugs from the Drugbank
database for which a chemical structure, designated route of administration (ROA), and an ATC code are available. Only entries with >35 drugs/
ATC class are considered for this analysis.
For each subsection below, we attempt to summarize the
impact of a compound’s acidity or basicity on a variety of properties and add some new insights. We apply statistical analyses
to observations presented, as this has not often been the case in
some previous analyses. Many papers do not, in fact, describe
how they define acids or bases, while other papers simply represent ionization at physiological pH without any quantitation.
In the next section, we will explore the behavior and properties of acids and bases as they apply to several key aspects of
drug discovery including target interactions, selectivity, DMPK,
safety, and biopharmaceutical properties. However, as mentioned above regarding lipophilicity, it may be difficult to
ascribe a specific molecular behavior solely to a compound’s
acidity or basicity.
dx.doi.org/10.1021/jm501000a | J. Med. Chem. 2014, 57, 9701−9717
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data mining activities. In addition, freely available drug databases
such as DrugBank21 and ChEMBL provide chemical structures, as well
as information extracted from package inserts (dose, route of
administration, warnings, etc.) for marketed drugs. In the interest of
reproducibility, we have restricted our analyses to data extracted from
public sources.
The drug subsets used in this paper were extracted from the
ChEMBL (Figure 1) and DrugBank (Figures 2, 4, and 10) databases.
In each case, we made an initial selection of all approved drugs. This
set was then further limited by removing compounds that violated any
of the following rules:
(1) fewer than 10 heavy atoms,
(2) molecular weight greater than 1000,
(3) contains an atom type other than H, C, O, N, S, P, F, Cl, Br, I,
Na, K, Mg, Ca, Li.
Where drugs were indicated as prodrugs in the “description” field of
the DrugBank database, we performed literature searches to identify the
active form of the drug and used the active form for all subsequent
analyses. The final drug sets consisted of 811 drugs from ChEMBL and
967 drugs from Drugbank.
Another issue with previous publications that compare acid/base
properties of molecules in drug discovery programs is the lack of a
clear definition of acids and bases. The criteria used to define acids and
bases can have a significant impact on the fraction of acidic, basic,
neutral, and zwitterionic compounds in data sets. As an example, in
Figure 2, we have carried out an analysis of 967 drugs extracted from
the DrugBank database. We list the number of compounds by charge
state based on three different criteria for ionization at pH 7.4. In the
first column we use a more relaxed criteria and classify compounds as
acidic, basic, or zwitterionic based on whether they are at least 10%
ionized at pH 7.4. In the second and third columns, we carry out a
similar analysis, using successively more stringent ionization criteria.
It is interesting to note how the number of bases and zwitterions
decreases dramatically as the criteria become more stringent. This is
because many of the drugs are weak bases that are only marginally
ionized at pH 7.4. Most of the acidic drugs are stronger acids, so the
number of acids changes only slightly. The increase in the number
of acids between the 50% and 90% cutoffs can be attributed to
zwitterions with weakly basic centers that are subsequently classified as
acids when the ionization criteria become more stringent. On the basis
of this analysis, we classify acids as compounds that are able to donate
a proton and are at least 50% ionized at pH 7.4. Similarly, we classify
bases as compounds that are able to accept a proton and are at least
Figure 5. Aqueous solubility data based on 37 100 compounds26
extracted from PubChem. Data points are colored by ClogD7.4 (green,
<2; yellow, 2−4; red, >4). A point in the grid indicates a statistically
significant difference (p < 0.05 based on a pairwise Wilcoxon rank test
using Holm’s method as a correction for multiple testing).
In the past, analyses of the properties of druglike molecules were
primarily restricted to proprietary data extracted from pharmaceutical
company databases. While the results of these analyses have been
highly influential, it has been difficult for those carrying out subsequent
research to reproduce and extend this work. Fortunately, over the past
few years a number of public bioactivity databases such as ChEMBL19
and PubChem20 have appeared. These databases contain millions of
chemical structures, associated biological activities, and physical properties that can provide valuable source materials for a wide variety of
Figure 6. Hepatic and renal clearance for the various ionization classes. Hepatic clearance data (591 unique compounds) and renal clearance data
(471 unique compounds) were extracted and combined from refs 27 and 28.
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Table 1. Literature Summary: Effects of Ionization State on DMPK Properties
permeability/P-gp efflux
oral absorption/
volume of distribution
protein binding
tissue distribution
key observations from refs reviewed
refs reviewed
- Ionized form of a drug is considerably more water-soluble with acids generally more
soluble than bases, possibly due to an overall greater extent of ionization for most
acids at physiological pH.
- Solubility affects almost all aspects of drug behavior and characterization from in vitro
assay reproducibility to bioavailability and formulatability (see Form/Formulatability
- Ionized molecules at physiological pH tend to interact with negatively charged lipid
membranes generally resulting in a low permeability: neutrals > bases > zwitterions >
- Increasing lipophilicity may increase permeability, especially for acids, bases, and
- P-gp efflux was influenced to some extent in the following order: zwitterions> neutral
≃ bases > acids: the number of HBD (as well as MW) might be the predominant
- Acids generally have higher oral bioavailability than bases in spite of poorer
permeability, possibly because of better solubility and lower clearance.
- Acidic and neutral drugs may tolerate greater lipophilicity; a potentially interesting
measure of lipophilicity for ionized species with regard to oral absorption by passive
diffusion is the distribution coefficient (log D) at pH 6.5, which is the approximate pH
of the small intestine, where absorption mostly takes place.
- Bases tend to be ionized in the GI tract, thus possessing higher polarity and reduced
lipophilicity, limiting passive absorption across biomembranes.
- Zwitterions tend to have low bioavailability.
- Hepatic: Acids generally have lower in vivo clearances than neutral and zwitterionic
molecules followed by bases, probably because of higher plasma protein binding of
acidic molecules.
- Renal: Both acids and bases were subject to significantly greater renal clearance than
neutral or zwitterionic molecules.
- Acids generally have lower Vd values, also due to higher plasma protein binding.
- Basic molecules often have higher Vd values (due to lower protein binding) favoring
increased half-life. These compounds can show significant interorgan variation.
- Acids > neutrals > zwitterions > basic molecules.
- Since acids typically have higher plasma protein binding and thus lower Vd values,
they may require high metabolic stability in order to obtain acceptable half lives.
- Bases may not bind as strongly as acids to plasma proteins probably because of their
affinity for negatively charged membranes/tissues (see tissue distribution section).
- Increasing lipophilicity may also increase protein binding regardless of ionization class.
- In general, only minor differences were found between the binding of acidic, basic,
neutral, and zwitterionic substances to various tissues, although basic drugs tend to be
stored in tissues with a pH that is lower than their pKa values (e.g., lung).
- Because of the lower pH in certain tissues, there would be a greater fraction of ionized
basic species that would electrostatically interact with the negatively charged cell
constituents (i.e., membrane phospholipids and/or acidic cellular compartments in
which accumulation may occur).
- Binding of bases was stronger to hepatocytes compared to neutral or acidic molecules
for a given log P.
- Tissue distribution of basic drugs is also highly dependent on lipophilicity.
- Only minor differences in binding to brain tissue was found between acidic, basic,
neutral, and zwitterionic substances, more influenced by lipophilicity. This is in
contrast to overall CNS penetration for which basic substances showed greater brain
exposures than neutrals followed by zwitterions and then acids.
- Basic amines tend to be FMO substrates (although there is some overlap, e.g.,
CYP2D6), whereas nonbasic compounds tend to be oxidized by CYPs, with the
degree of N-substitution also playing a distinct role. Tertiary amines are generally
FMO substrates in contrast to primary amines that are generally better CYP
substrates; secondary amines are less clear-cut.
1, 2, 5, 6
1, 5, 6, 16, 18, 37
1, 5, 6, 38
1, 5, 6, 24
4, 5, 29, 30
1, 4−6, 29, 30
1−3, 5, 6, 29−36, 53
1, 6, 25, 52
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Table 1. continued
key observations from refs reviewed
refs reviewed
- In the case of N-oxidation, when a molecule possesses more than one basic nitrogen
atom, the preferred site is generally the most basic center.
- CYP2D6 tends to prefer basic substrates; basic compounds tended to also be the most
potent inhibitors, followed by zwitterions, neutral molecules, and finally acidic
- CYP2C9 prefers neutral and acidic substrates, and these same classes tend to be the
more potent 2C9 inhibitors. Interestingly, while 2C9 and 2C19 are structurally similar,
2C19 does not have the same affinity for acids as that found for 2C9.
- CYP3A4 is involved in the metabolism of a variety of substrates. With regard to
inhibition, neutral molecules tend to be the most predominant inhibitor class followed
by bases and zwitterions and then acids. Lipophilicity was also an important driver of
3A4 inhibition.
- MAO substrates range from weakly basic to highly basic (primary amines, also some
secondary and tertiary amines). Inhibitors tend to follow the same pattern.
channel, etc.) presents an opportunity to examine the relationship between protonation state and target class. An analysis of
bioactivity data from the ChEMBL database with a reported
IC50, EC50, or Ki value was performed, wherein each compound
was classified as an acid, base, neutral, or zwitterion. Figure 3
shows the distribution of protonation states across all target classes
as well as the four most populated classes. It was found that there
were a greater relative proportion of basic compounds among
membrane receptor and transporter targets while neutral
compounds predominated among enzyme and ion channel
targets. To ensure that this observation is not strongly biased by
intrinsic activity, we plotted the data using two different cutoff
values, one allowing only highly active compounds (less than
100 nM, 361 104 compounds) and another value allowing a
wider range of activity (less than 5 μM, 844 368 compounds).
In both cases, the distribution of acids, bases, neutrals, and
zwitterions across target classes was quite similar.
A similar analysis was then performed across therapeutic
areas. One means of identifying the therapeutic class for a drug
is the anatomical therapeutic chemical (ATC) classification
system established by the World Health Organization. The
ATC classification system divides drugs according to their
target organ or system. Using the ATC codes from Drugbank,
we were able to extract 10 therapeutic areas representing a total
of 967 drugs (Figure 4). It was somewhat surprising to us that
similar analyses are rare in the literature with the closest such
analysis being that of Varma et al.24 in which 391 compounds
were classified into several therapeutic areas and analyzed by
ionization state.
Figure 4 shows that in the majority of cases, neutral compounds are the predominant species. A greater proportion
of acidic molecules are observed among systemic anti-infective
drugs and drugs used to treat musculoskeletal diseases.
The vast majority of acidic drugs (34 of 40) classified as
“antiinfectives for systemic use” are subclassified as “antibacterials for systemic use”. These drugs primarily consist of
sulfonamide and β-lactam antibiotics. Of the 49 acidic compounds targeting the musculoskeletal system, 20 are classified
as “antiinflammatory and antirheumatic products”. These primarily include NSAIDS such as indomethacin and ketorolac, as
well as bisphosphonates such as zoledronate and clodronate,
used for the treatment of osteoporosis and other bone diseases.
Basic molecules are predominantly observed in the CNS and
respiratory therapeutic areas. Of the 108 basic compounds
classified as affecting the CNS, 56 are classified as psychoanaleptics
50% ionized at pH 7.4. It should be noted, however, that even a
relatively small degree of un-ionized species may, in some cases, be
responsible for observed biological/pharmacological effects, thus
complicating some of the following analyses.
We used the pKa plugin in version 6.2 of ChemAxon’s cxcalc
program22 to calculate pKa values. These pKa values were then used to
calculate percent ionization and classify molecules as acidic, basic, neutral,
or zwitterionic. A recent publication by Settimo72 evaluated the accuracy
of the ChemAxon pKa predictor against a number of pharmaceutically
relevant data sets. The authors reported a median absolute deviation
(MAD) of 0.33−0.37 for acids and 0.31−0.64 for bases. While all pKa
prediction methods are imperfect, we believe that the reported resolution
is sufficient to classify compounds as acidic, basic, neutral, or zwitterionic.
When comparing properties of druglike molecules, many authors
provide plots that simply compare the mean or median of a particular
distribution (e.g., basic versus neutral compounds). While this type of
representation can sometimes convey an overall trend, it often obscures
the true nature of the distribution. In order to better capture overall
property distributions, we use either boxplots or beeswarm plots to
represent distributions. Data represented in boxplots denote the median
as a solid thick line, and the whiskers define the upper and lower 1.5
interquartile ranges. A beeswarm plot extends this representation of
the full data distribution with closely packed, nonoverlapping points.
Data points are colored by ClogD7.4 (green, <2; yellow, 2−4; red, >4).
We also performed analyses with the data points colored by ClogP and
by molecular weight (see ref 83), although we do not believe this has
provided any additional insights of significance.
In comparing distributions, one typically wants to establish the
statistical significance of the difference between mean values or
medians. In many cases, researchers carry out an analysis of variance or
ANOVA to evaluate the significance of differences between mean
values. While ANOVA is a valid approach in many situations, the
method assumes that the data are normally distributed. As can be seen
in our subsequent analyses, the activity and property distributions
found in drug discovery databases often do not follow a normal
distribution. As such, we have used a nonparametric method, which is
less sensitive to the data distribution, as well as to the presence of
outliers. In order to provide the reader with a simple method of
visualizing the statistical significance of comparisons, we have placed a
grid below each beeswarm plot. A point in the grid indicates a statistically significant difference (p < 0.05 based on a pairwise Wilcoxon
rank test using Holm’s method as a correction for multiple testing). All
data visualization and statistical calculations were performed with R,
The fact that each assay in the ChEMBL database is annotated
with a drug target class (enzyme, membrane receptor, ion
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Table 2. Literature Summary: Effects of Ionization State on Safety Properties
cellular distribution
transporter inhibition
reactive metabolites
key observations from refs reviewed
refs reviewed
- Basic compounds are generally known to bind to a variety of receptors and
transporters. Highly basic positively charged compounds are generally more prone
to promiscuity than neutral, zwitterionic, or acidic compounds. Increased
lipophilicity also generally contributes to overall promiscuity.
- hERG inhibition: It is well established that basic compounds generally have a high
propensity for inhibition of hERG potassium channels, followed by zwitterions.
Neutral and acidic compounds generally show lower amounts of inhibition.
Inhibition is also strongly driven by lipophilicity.
- Bases often become sequestered in acidic organelles of many different cell types and
may thereby contribute to various toxicities.
- Phospholipidosis: Lipophilic basic molecules, especially cationic amphiphiles, can
cause phospholipidosis by distributing to membranes and lysosomes and can also be
influenced by overall compound lipophilicity.
- Mitochondrial toxicity: Both strongly acidic and basic drugs have been associated
with mitochondrial dysfunction. Strong lipophilic acids tend to have the greatest
propensity to cause uncoupling of oxidative phosphorylation, while basic drugs can
accumulate into the acidic mitochondirial cytosolic space in tissues such as liver and
- Erythrocytes: Basic drugs can partition into red blood cells driven by the
electrostatic attraction to the negatively charged phosphatidylserine component of
RBC membranes. Increased lipophilicity has also been noted as a contributing factor.
- BSEP: Substrates are monovalent, negatively charged acids, and while 30−40% of
inhibitors are acidic, the majority of BSEP inhibitors are un-ionized. Positively
charged compounds tend to be negatively correlated with BSEP inhibition.
- OATPs: Inhibitors tend to be strong acids (especially carboxylate containing
molecules). Additional features contributing to OATP inhibition are lipophilicity
and number of hydrogen bond acceptors.
- OCTs: The most important features for OCT inhibition appear to be lipophilicity
and positive charge.
- Acids: Some O-acyl glucuronide metabolites of carboxylic acids can covalently
modify proteins either through a transacylation reaction or via acyl migration and
have been implicated in various ADRs and idiosyncratic toxicities. In addition to acyl
glucuronide formation, some carboxylic acids can also be bioactivated to
electrophilic acyl-coenzyme A thioester derivatives (acyl-CoAs).
- Bases: Cyclic amines such as piperidines can be oxidized to reactive iminium species
via oxidative dehydration of the piperidine ring. Piperazines can also form reactive
iminium and nitrenium species through a similar mechanism. Additionally, some
piperazines can be oxidized to form a potentially reactive conjugated imine−amide
1, 6, 14, 39, 42
1, 31, 34, 43−51
41, 58−61
zwitterions, and neutrals presented in Figure 4 across
therapeutic areas may be a function of tissue distribution in
addition to other factors.
or psycholeptics; examples include antidepressants such as
fluvoxamine, citalopram, and reboxetine. It has been previously
observed that the majority of drugs that enter the CNS and
demonstrate pharmacological effects tend to be basic.1−3,5,6,37
The majority (26 of 34) of basic compounds targeting the
respiratory system are classified as either “antihistamines for
systemic use” (e.g., clemastine, doxylamine) or “drugs for
obstructive airway diseases” (e.g., isoetarine, orciprenaline,
terbutaline). Note that the data presented in Figure 4 also
represent multiple routes of administration. For example, 28 of
the compounds affecting the respiratory system are orally delivered
and 18 are delivered nasally or thorough inhalation. An additional
10 drugs are delivered through other routes.
While Figure 4 shows trends for some therapeutic areas, it
also makes it clear that multiple ionization classes have been successful in most areas. As previously suggested in the Introduction
and expanded upon in the following section, the impact
of ionization state can influence both cellular and tissue
distribution. It is possible that the distribution of acids, bases,
The ionization state of a drug is of fundamental importance
with regard to DMPK because it modulates lipophilicity,
solubility, and metabolism.1−6,14,25 Table 1 provides a summary
of the literature with respect to the effects of acidity or basicity
on a variety of DMPK properties. Most of our analyses based
on ChEMBL, Pubchem, and Drugbank compounds agree with
the literature trends shown in Table 1. One area in which we
observed a slight difference with reported analyses was for
aqueous solubility. The observation that acids were generally
more soluble than bases was drawn from a set of approximately
44 500 GSK compounds.5 The current analysis includes 37 100
acids, bases, neutrals, and zwitterions derived from a PubChem
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Figure 7. Selectivity data based on 3282 compounds from ChEBML 18 that were tested in at least 20 assays. The selectivity ratio is defined as the
number of assays active/number of assays tested. Two different activity cutoffs (IC50 or Ki of <100 nM and IC50 or Ki of <5 μM) are considered.
Figure 8. OATP inhibition data for 224 compounds40 derived from ChEMBL 18.
data set. This data set26 is a subset of the NIH Molecular
Libraries Small Molecule Repository (MLSMR) for which
kinetic solubility measurements have been determined. Figure 5
shows that all ionized species (i.e., acids, bases, and zwitterions)
generally show better solubility than neutrals and that, for this
data set, basic compounds may even possess a slight (yet
statistically significant) advantage in solubility over acidic
molecules. Regardless of such slight differences between data
sets, the main lesson to be extracted is that both ionized acids
and bases generally possess greater solubility than neutral
molecules and that lipophilicity also generally plays an
important role.
Regarding both in vivo hepatic and renal clearance, we
performed an analysis on two published data sets27,28 in which
we combined the hepatic and renal data resulting in 591 unique
compounds in the hepatic clearance set and 471 unique compounds in the renal clearance set. Figure 6 suggests agreement
between these results and those previously reported in which
acids generally demonstrated lower hepatic clearances than the
other ionization classes. In addition to the generally accepted
view that protein binding plays an important role in influencing
hepatic clearance especially for acidic compounds, we observed
that higher hepatic clearances among basic and neutral
compounds were generally also associated with a higher degree
of lipophilicity. For renal clearance, the more polar ionized
acids, bases, and zwitterions generally showed higher clearances
than neutral molecules. Among all compounds that are cleared
renally, lipophilicity tended to be generally low.
An interesting observation from this work relates to the distribution of basic drugs into certain tissues and cellular compartments. In general, tissue distribution depends on competition of compound binding to blood versus tissues, as well as
within individual tissues.29 Acidic drugs tend to be highly
bound to plasma proteins and are most often present in tissue
extracellular water. Depending on the degree of ionization (due
to overall acid strength), these compounds can also distribute
to adjacent tissues. Lipophilic basic drugs tend to be stored
in tissues that are rich in acidic phospholipids and in acidic
cellular organelles such as lysozymes (e.g., liver, lung, kidney).30
In these acidic organelles, some basic compounds become
protonated and, thus, sequestered. Drug distribution in cells is
a fundamental, yet often overlooked aspect in drug efficacy.
Accumulation of lipophilic basic compounds into lysosomes
(lysosomotropism) and other acidic cellular organelles (e.g.,
mitochondria, endosomes, golgi, Figure 1) may also contribute
to safety issues.31 Lysosomal trapping may also play a role in
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Figure 9. Cellular potency data (IC50 or EC50) based on 400 397 data points from cell assays (62 839 compounds extracted from ChEMBL 18).
All compounds have activity between 1 nM and 100 μM.
Table 3. Literature Summary: Effects of Ionization State on Dosage Form/Formulatability
oral dosage form
iv dosage form
key observations from refs reviewed
refs reviewed
- Basic compounds are generally more ionized at gastric pH (∼1.5) and thus soluble in the
stomach. However, as these compounds transit into the small intestine and the pH
increases toward 6.5, the solubility tends to rapidly decrease, thereby reducing solubility
and thus absorption. Solubility in simulated intestinal fluid (SIF, pH 6.8) can provide
insights into oral absorption.
- Salt and cocrystal formation: relevant to both oral and iv dosage forms. Salts and cocrystals
can increase the overall solubility (by increasing the dissolution rate) and bioavailability of
otherwise intractable compounds. Appropriate salts/cocrystals can produce a crystalline
form of low hygroscopicity, high melting point, good mechanical properties, and acceptable
chemical stability. The relative acidity or basicity of the drug substance and its salt-forming
counterion will determine if a stable solid salt can be prepared and isolated. A greater
number of salts exist for basic drugs relative to acidic drugs.
- Formulation of an ionizable poorly water-soluble drug may require an extreme pH value in
order to get adequate solubility.
- The preferred pH range for iv formulations is 4−8, to minimize pain/tissue damage on
the efficacy and resistance of small molecule anticancer
agents. In some drug sensitive tumors there appears to be a
defective lysosomal acidification mechanism that can lead
to an increase in the therapeutic concentration of weakly basic
drugs in the cytoplasm and nucleus.13,32 However, some
resistant tumors are thought to sequester basic drugs in the
lysosomes and other acidic organelles. This is in contrast
to the differential pH gradient observed between many tumor
and normal cells. While the intracellular pH is roughly the
same for both tumor and normal cells (pH ≈ 7.2), it has
been reported33 that the immediate extracellular pH is
consistently lower in tumor by ∼0.4 unit relative to normal
tissue. This differential cellular pH gradient in tumor tissue
may provide an opportunity to selectively modulate the pKa of
small molecule oncology agents and take advantage of this
9, 10, 65, 66
1, 81
As with many other aspects of drug action, toxicological effects
may be the result of factors other than simple molecular properties such as acidity or basicity. Certainly, factors such as
mechanism, metabolism, structural features, and total daily dose
can have a significant impact on safety outcomes both clinically
and preclinically. Nonetheless, there have been some empirical
observations that suggest a contribution to toxicology outcomes
based on a compound’s acidity or basicity. Table 2 represents a
summary of these findings from the literature.
With respect to selectivity, the literature consensus suggests
that basic compounds tend toward higher degrees of
promiscuity39 and that lipophilicity also tends to contribute
to lower selectivity. We performed an analysis of 3282 compounds from ChEMBL that were tested in at least 20 assays
using two different activity cutoffs, one allowing only highly
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Figure 10. Route of administration data based on a set of 1062 drugs from the Drugbank database for which a chemical structure, designated route of
administration, and an ATC code were available. Only entries with at least 20 drugs/ROA were considered for this analysis.
Figure 11. Select examples from Table 4.
active compounds (less than 100 nM) and another value allowing a wider range of activity (less than 5 μM). Ideally, this
analysis would have been performed on a large set of compounds that had been run through the same set of assays. Our
objective is to make some general observations regarding the
selectivity of acids, bases, zwitterions, and neutral compounds.
Hopefully, as more public data becomes available, we and
others will be able to extend this analysis.
In both cases (Figure 7), we found that basic compounds
were generally less selective than acidic or neutral compounds;
the number of zwitterionic molecules is likely too small to draw
any meaningful generalizations. We did not observe a
significant effect of lipophilicity with respect to lower selectivity
using a cutoff of 100 nM, although there appears to be some
contribution of lipophilicity toward lower selectivity of acids
and bases using the 5 μM activity cutoff.
Some of the interesting aspects of cellular distribution for
basic compounds have been discussed in the above section on
DMPK properties. The main point worth emphasizing in the
context of safety is simply that accumulation of basic molecules
in acidic rich organelles of certain tissues such as liver, lung, and
pancreas can be a factor in inducing toxicity in these organs.
In recent years, it has become more common to evaluate
the effects of lead molecules for their potential to inhibit several
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Table 4. Literature Summary: Select Examples of pKa Modulation
tumor targeting
overcoming safety
improving selectivity
improving CNS
impacting tissue
key observations from refs reviewed
refs reviewed
- Purposeful targeting of anticancer drugs to intracellular compartments in cancer cells.
- HSP90 inhibitors targeting lysosomal acidification defect: A series of geldanamycin (GDA)
analogs was synthesized to determine whether compound pKa could be optimized to take
advantage of this defect in several cancer cell lines. An elevation in lysosomal pH was
predicted to have a profound impact on the intracellular distribution of weakly basic amines
that are substrates for ion trapping in lysosomes. It was found that a GDA analog with a pKa
of 8.1 had the maximum degree of selectivity for HL-60 leukemic cells versus normal human
- pH sensitive magnetic nanoparticles that can act as both diagnostic and therapeutic agents
(theranostics) by taking advantage of the pH gradient between the tumor microenvironment
(∼pH 6.8) and that of endosomes/lysozymes (pH 5.0−5.5). These agents target tumors via
surface-charge switching triggered by the acidic tumor microenvironment and are further
disassembled into a highly active state in acidic subcellular compartments. Small tumors
implanted in mice were successfully visualized via unique pH-responsive T1MR contrast and
fluorescence, demonstrating early stage diagnosis of tumors without using any targeting
agents. Furthermore, pH-triggered generation of singlet oxygen enabled pH-dependent
photodynamic therapy to selectively kill cancer cells including efficacy against heterogeneous
drug-resistant tumors.
- Modification of a small molecule Met inhibitor, GEN-203 (N-ethyl-3-fluoro-4-aminopiperidine), with significant liver and bone marrow toxicity in preclinical species with the
intention of increasing the safety margin: The basicity and high lipophilicity of GEN-203 were
hypothesized to drive the high distribution of this compound to tissues and subsequent
toxicities of the compound. The basicity of GEN-203 (pKa = 7.45) was decreased through
addition of a second fluorine in the 3-position of the aminopiperidine to yield GEN-890 (Nethyl-3,3-difluoro-4-aminopiperidine, pKa = 5.93, Figure 11A). This minimal structural change
led to a decreased volume of distribution of the compound in mouse approximately 4-fold
(from 3.6 to 0.99 L/kg) and maintained cell potency against the target kinase, Met. Most
importantly, GEN-890 showed comparable efficacy in a xenograft model and did not cause
detectable liver or bone marrow toxicity in mice up to doses of 600 mg/kg after 14 days of
daily dosing at plasma exposures that were comparable to or exceeded the exposures achieved
with GEN-203.
- The incorporation of a carboxylic acid within in a series of 3-amido-4-aryl substituted
piperidines led to the discovery of potent, zwitterionic renin inhibitors with improved offtarget profiles (CYP3A4 time-dependent inhibition and hERG affinity) relative to analogous
nonzwitterionic inhibitors.
- Identification of 2-morpholin- and 2-thiomorpholin-2-yl-1H-benzimidazoles as selective and
CNS penetrating H1-antihistamines for insomnia: In the lead optimization process, the pKa
and/or log P values of benzimidazole analogs were reduced by attachment of polar
substituents to the piperidine nitrogen or incorporation of heteroatoms into the piperidine
heterocycle. Select morpholine and thiomorpholine analogs demonstrated improved
selectivity and CNS profiles compared to the original lead and are potential candidates for
evaluation as sedative hypnotics. A reduction in pKa for these compounds (from 9.1 to ∼7.4)
was presumed to be responsible for the increased CNS exposure relative to the initial starting
- The introduction of a difluoroethyl side chain lowered the pKa of a guanidine-based 2-amino
dihydroquinazoline 5-HT5AR antagonist (from 9.9 to 8.9) and resulted in a significantly
improved brain-to-plasma ratio, enhancing the pharmacological utility of these compounds.
By modulation of the lipophilicity and pKa, a 20-fold increase in brain-to-plasma ratio could
be achieved, leading to micromolar brain concentrations after oral administration.
- By adjustment of the pKa of basic nitrogen containing cathepsin S inhibitors, a set of
compounds with pKa of 6−8 were identified that maintained excellent cell-based activity and
avoided undesired sequestration in spleen leading to potential safety concerns. Cathepsin S
resides in the acidic lysosomes, and reducing cellular accumulation to the point where organ
level sequestration is not observed while maintaining cellular potency is a challenge. pKa
calculations were employed to inform design, and it was found that a heteroarylmethyl group
could reduce the basic piperidine nitrogen pKa into the 6−8 range. Some of these new analogs
(Figure 11B) maintained excellent cell activity and reduced the spleen/plasma ratio ∼13-fold
from 26 to 1.9.
32, 74, 75
67, 77
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Table 4. continued
getting the balance
key observations from refs reviewed
refs reviewed
- Example of lowering pKa (and log D) in an attempt to balance several simultaneous risks for a
basic molecule: A class of amidoacetonitrile cathepsin C inhibitors (Figure 11C) initially
showed hERG inhibition and phospholipidosis that translated to liver histology findings, in
vivo. Replacement of a basic piperidine with a quaternary tetrahydropyran lowered the pKa
and log D from 8.4 and 2.7 to 5.8 and 1.9, respectively. This THP analog demonstrated low
hERG activity, low activity in phospholipidosis assays, improved selectivity versus other
cathepsins, and a clean selectivity profile in receptor screening. This compound further
showed desirable PK properties and advanced into more detailed in vivo profiling studies.
key transporters, as this may impact both safety and efficacy.
Among these are the bile salt export pump (BSEP) and the
hepatic organic anion transporting polypeptides (OATPs).
Regarding OATP inhibition, we have expanded upon the
literature analysis discussed in Table 2 by considering data for
224 compounds40 included in ChEMBL. The data shown in
Figure 8 support the observation that OATP inhibitors tend to
be more highly acidic and that increased lipophilicity correlates
with increased OATP inhibition for basic and neutral molecules
more than for acidic molecules.
In addition to the topics covered in Table 2, it is also generally
appreciated that some anilines and anilides are associated with
mutagenicity, direct toxicity, methemoglobinemia, and immunogenic allergenic toxicity.41 Another topic that often comes up in
discussions regarding the inclusion of acidic and basic groups in
bioactive molecules is that acids generally tend to be less active
in cellular assays. The data shown in Figure 9 are in agreement
with those observations (i.e., zwitterionic > basic and neutral >
acidic compounds) but also highlight an important point, i.e.,
that there are still a significant number of acids with potent
cellular activity.
diversity of solid forms of drug substances exhibiting the
appropriate balance of critical properties required for development into viable and effective drug products.
We were also interested in whether ionization state influenced
route of administration (ROA). To this end, we performed an
analysis of ROAs for 967 drugs from the Drugbank database
(Figure 10). As one might expect, neutral and basic substances
tend to predominate in each category. The proportion of bases is
slightly less than neutrals for the intravenous route, and the
fraction of acids tends to be largest for the intramuscular route.
As with all molecular properties, pKa can be optimized to
accomplish specific goals and overcome certain undesirable
properties. While this may appear obvious, pKa modulation is
most often employed retrospectively to “fix a problem” and less
frequently employed prospectively as part of the initial SAR
exploration. Perhaps the most common example of pKa optimization is the attenuation of basicity to overcome a hERG
liability. This approach has been extensively described5,14,37,67−69
and, as mentioned earlier, is a situation where a reduction in
basicity is closely tied to a net reduction in lipophilicity.11,14
This relationship makes it difficult to understand which
property is more relevant or whether these properties can be
viewed in isolation. Additional examples of modulation of pKa
are included in Table 4.
It is interesting that in almost all of the examples presented in
Table 4, there was an effort to reduce the basicity of a lead
compound in an attempt to alter some undesirable compound
attribute. A variety of approaches were employed to lower the
pKa of these basic amines. A review by Morgenthaler et al.68
describes several simple and useful concepts for predicting and
tuning the pKa values of basic amine centers. The paper describes a variety of approaches (see Figure 12 for select examples)
While the pKa of a drug is only one important factor among
several with respect to its impact on drug behavior, it plays a
very significant role in the overall formulatability of a drug
substance for both oral and intravenous dosage forms. Table 3
summarizes some important attributes of pKa as it relates to
form and formulatability.
An early understanding of the pH−solubility profile of preclinical candidates can have a large impact on both iv and oral
formulatability and can provide insights into challenges that
might be faced in development. As mentioned in Table 3, salt
formation can be a useful approach to improve the solubility
of some drugs. Historically, the process of selecting the most
appropriate salt form of a drug substance has been approached
in a somewhat empirical manner.9 It is commonly believed that
when the pKa difference between an acid and a base is greater
than 2, often referred to as the “rule of 2”, a stable salt can be
formed.62,63 This rule is based on the assumption that salt
formation occurs when both acidic and basic species are present
in an environment favoring nearly complete ionization. While
this is an oversimplification, it does highlight the point that the
conjugate base of a weak acid must be moderately strong, and
vice versa. More recently, there has been increased utilization
of cocrystals in drug development. The distinction between a
salt and a cocrystal is whether proton transfer has occurred.
Cocrystals can be further thought of as structurally homogeneous crystalline materials containing two or more neutral
building blocks present in specific stoichiometric amounts.64
Cocrystals offer new opportunities for producing a greater
Figure 12. Modulation of amine basicity by select substituents. Figure
is modified from ref 68.
to reduce amine pKa values such as fluorine substitution, oxetanes,
and addition of a carbonyl group in the β-position. They highlight
the point that it is important not only to know the pKa values
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Journal of Medicinal Chemistry
Figure 13. Fraction of acidic, basic, neutral, and zwitterionic compounds per year in J. Med. Chem. papers (from 1980 to 2013, taken from ChEMBL
18). In each plot, the solid line is based on locally weighted scatterplot smoothing (Loess). This method uses weighted least squares to fit a set of low
degree polynomials to subsets of the data. Gray area represents 95% confidence interval. (A) Fraction acidic, basic, neutral, and zwitterionic
compounds. (B) Fraction of compounds (N = 417 998) with at least one aromatic nitrogen.
The inherent acidity or basicity of a given compound or lead
series can have a profound impact on a variety of drug attributes
including potency, physical properties, DMPK, cellular and tissue
distribution, selectivity, overall safety, and formulatability. While it
may not be possible, or practical, in many cases to deconvolute
the contribution of a compound’s acidity or basicity from other
properties such as lipophilicity, we believe some of the observations summarized in this review can provide useful guidelines. Some additional observations from this work include the
• The prospective modification of pKa should be considered along with other molecular properties to be
explored within the broader SAR optimization of a given
lead series. Computational tools that allow thought
exercises in pKa modification may be quite useful. The
(either from experiment or calculation) but even more so
to be able to modulate basicity in a rational manner. While
it is beyond the scope of this paper to provide a review on pKa
prediction programs, several recent studies provide a thorough
synopsis of available methods.70−72 We have found that tools
such as the pKa plugin for ChemAxon’s MarvinSketch22 can
be quite useful in this regard, as it provides not only predicted pKa values and major microspecies at a given pH but
also a quantitative prediction of % ionization across a userselected pH range. The MoKa program73 also allows for similar
analyses. In a further extension of using such tools, Diaz et al.45
calculated electrostatic potentials of predominant microspecies at different pH values corresponding to different
cellular organelles in an attempt to gain additional insights
into the impact of structural changes on basicity and cellular
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vast majority of literature examples of pKa modification
during lead optimization involve reducing basic pKa
values. However, one could imagine situations in which
the pKa for very weakly basic compounds might benefit
from being raised to ∼6.0, e.g., for specific indications
such as tumor targeting. We further believe there is an
opportunity for greater use of ClogD calculations performed at different pH values, reflecting the pH of
certain cellular and tissue compartments of interest.
Experimental determinations of both pKa and log D are
amenable to greater throughput than in the past and
should be obtained for a greater number of compounds
within a series. These experimentally determined values
not only provide a valuable assessment of these properties but also help calibrate the accuracy of the calculated values within a given series.
• Weakly basic compounds are generally acceptable, and a
large percentage of pharmacologically active compounds
possess N-containing heterocycles of one type or
another. It is interesting that the number of basic compounds reported in J. Med. Chem. decreased during the
1980s with a commensurate rise in the number of
neutrals over that same time period (Figure 13A). Both
classes have leveled off since that time. It appears that the
number of acidic molecules declined slightly as well from
1990 to 2000 and has since leveled off. During this same time
period (i.e., 1980−2013) however, there has been a steady
increase in the incorporation of nitrogen-containing heterocycles in many of our chemical explorations (Figure 13B).
• It may be advisible to avoid strongly basic (i.e., pKa > 8.4,
as defined by >90% ionization at pH 7.4) compounds
even for CNS projects where basic compounds have
classically been optimal for GPCR targets, and to consider opportunities where allosterism or other approaches
might offer an optimization path. If a target or phenotypic
optimization path absolutely requires a strongly basic
center for primary activity, efforts may benefit from a
thorough characterization of potential selectivity, safety,
and distribution issues as early as possible in an attempt to
arrive at an appropriate balance of properties. In some
cases, lowering the pKa into the 6−7 range may obtain the
desired balance.
• There is an opportunity to consider a greater use of acidic
functionality in chemical explorations. While it may sometimes require the synthesis of multiple challenging analogs
to obtain the desired cellular potency,80 there can be potential advantages (e.g., selectivity, DMPK) to be obtained.
Further, it might be advantageous to consider the
addition of a greater number of acidic compounds to
screening collections.
Chemistry in 1988 from the University of North Carolina at Chapel
Hill under the guidance of Professors Steven D. Wyrick and J. Phillip
Bowen. After 2 years as a postdoctoral fellow in Theoretical and
Computational Chemistry with Professor Lee Pedersen, also at
University of North Carolina at Chapel Hill, he joined Glaxo/Glaxo
Wellcome where he stayed until joining Vertex in 1997. At Vertex, Dr.
Charifson has led several drug discovery teams in antibacterials,
antivirals, and oncology.
W. Patrick Walters is a Principal Research Fellow at Vertex
Pharmaceuticals in Boston, MA, where he has worked since 1995.
He heads the Computational Sciences group, which encompasses
molecular modeling, cheminformatics, bioinformatics, and scientific
intelligence. Dr. Walters is a member of the editorial advisory boards
for the Journal of Medicinal Chemistry, Molecular Informatics, and Letters
in Drug Design & Discovery. Before joining Vertex, Dr. Walters earned
his Ph.D. in Organic Chemistry from the University of Arizona where
he studied the application of artificial intelligence in conformational
analysis. Prior to obtaining his Ph.D., he worked at Varian Instruments
as both a chemist and a software developer. Dr. Walters received his
B.S. in Chemistry from the University of California, Santa Barbara.
The authors thank Jeremy Green and Jim Empfield for a
thorough critique of this work as well as providing insightful
ANOVA, analysis of variance; ATC, anatomical therapeutic
chemical; BSEP, bile salt export; CNS, central nervous system;
CYP, cytochrome P450; DMPK, drug metabolism and
pharmacokinetics; FMO, flavin-containing monooxygenase;
GDA, geldanamycin; GI, gastrointestinal; GPCR, G-proteincoupled receptor; HBD, hydrogen bond donor; hERG, human
ether-a-go-go-related gene; log D, distribution coefficient; H1,
histamine H1 receptor; 5HT5AR, 5-hydroxytryptamine receptor
5A; iv, intravenous; LOESS, locally weighted scatterplot
smoothing; log P, partition coefficient; MW, molecular weight;
MLSMR, Molecular Libraries Small Molecule Repository;
OATP, organic anion transporting polypeptide; OCT, organic
cation transporter; ROA, route of administration; SIF,
simulated intestinal fluid; T1MR, T1-weighted magnetic
resonance; Vd, volume of distribution
(1) Manallack, D. T.; Prankerd, R. J.; Yuriev, E.; Oprea, T. I.;
Chalmers, D. K. The Significance of Acid/Base Properties in Drug
Discovery. Chem. Soc. Rev. 2012, 42, 485.
(2) Manallack, D. T. The pKa Distribution of Drugs: Application to
Drug Discovery. Perspect. Med. Chem. 2012, 1−23.
(3) Manallack, D. T. The Acid−Base Profile of a Contemporary Set
of Drugs: Implications for Drug Discovery. SAR QSAR Environ. Res.
2009, 20, 611−655.
(4) Leeson, P. D.; St-Gallay, S. A.; Wenlock, M. C. Impact of Ion
Class and Time on Oral Drug Molecular Properties. Med. Chem.
Commun. 2011, 2, 91.
(5) Gleeson, M. P. Generation of a Set of Simple, Interpretable
ADMET Rules of Thumb. J. Med. Chem. 2008, 51, 817−834.
(6) Meanwell, N. A. Improving Drug Candidates by Design: a Focus
on Physicochemical Properties as a Means of Improving Compound
Disposition and Safety. Chem. Res. Toxicol. 2011, 24, 1420−1456.
(7) Lesney, M. S. A Basic History of AcidFrom Aristotle to Arnold.
Today’s Chemist at Work; American Chemical Society: Washington,
DC, 2003; pp 47−48.
(8) Kolb, D. Acids and Bases. J. Chem. Educ. 2004, 459−464.
Corresponding Author
*E-mail: paul_charifson@vrtx.com. Phone: 617-341-6442.
Author Contributions
Both authors contributed equally to this work.
The authors declare no competing financial interest.
Paul S. Charifson is a Senior Research Fellow in Medicinal Chemistry
at Vertex Pharmaceuticals in Boston, MA, where he has worked since
1997. Before joining Vertex, Dr. Charifson earned a Ph.D. in Medicinal
dx.doi.org/10.1021/jm501000a | J. Med. Chem. 2014, 57, 9701−9717
Journal of Medicinal Chemistry
(9) Brittain, H. G. Strategy for the Prediction and Selection of Drug
Substance Salt Forms. Pharm. Technol. 2007 (http://www.pharmtech.
(10) Stephenson, G. A.; Aburub, A.; Woods, T. A. Physical Stability
of Salts of Weak Bases in the Solid-State. J. Pharm. Sci. 2010, 100,
(11) Waring, M. J.; Johnstone, C. A Quantitative Assessment of
hERG Liability as a Function of Lipophilicity. Bioorg. Med. Chem. Lett.
2007, 17, 1759−1764.
(12) Foye’s Principles of Medicinal Chemistry, 7th ed.; Lemke, T. L.,
Williams, D. A., Roche, V. F., Zito, S. W., Eds.; Lippincott Williams &
Wilkins: Philadelphia, PA, 2013; pp 67−69 and Appendix B.
(13) Altan, N.; Chen, Y.; Schindler, M.; Simon, S. M. Defective
Acidification in Human Breast Tumor Cells and Implications for
Chemotherapy. J. Exp. Med. 1998, 187 (10), 1583−1598.
(14) Jamieson, C.; Moir, E. M.; Rankovic, Z.; Wishart, G. Strategy
and Tactics for hERG Optimizations. Antitargets: Prediction and
Prevention of Drug Side Effects; Wiley-VCH: Weinheim, Germany,
2008; Chapter 18, pp 423−455.
(15) Notari, R. E.; Ed. Biopharmaceutics and Clinical Pharmacokinetics: An Introduction, 4th ed.; Marcel Dekker: New York, 1987; pp
(16) Pagliara, A.; Reist, M.; Geinoz, S.; Carrupt, P. A.; Testa, B.
Evaluation and Prediction of Drug Permeation. J. Pharm. Pharmacol.
1999, 51 (12), 1339−1357.
(17) Cramariuc, O.; Rog, T.; Javanainen, M.; Monticelli, L.;
Polishchuk, A. V.; Vattulainen, I. Mechanism for Translocation of
Fluoroquinolones Across Lipid Membranes. Biochim. Biophys. Acta,
Biomembr. 2012, 1818, 2563−2571.
(18) Mazák, K.; Noszál, B. Zwitterions Can Be Predominant in
Membrane Penetration of Drugs: Experimental Proof. J. Med. Chem.
2012, 55, 6942−6947.
(19) Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.;
Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.;
Overington, J. P. ChEMBL: A Large-Scale Bioactivity Database for
Drug Discovery. Nucleic Acids Res. 2012, 40 (D1), D1100−D1107.
(20) Li, Q.; Cheng, T.; Wang, Y.; Bryant, S. H. PubChem as a Public
Resource for Drug Discovery. Drug Discovery Today 2010, 15 (23),
(21) Wishart, D. S.; Knox, C.; Guo, A. C.; Shrivastava, S.; Hassanali,
M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: A Comprehensive
Resource for in Silico Drug Discovery and Exploration. Nucleic Acids
Res. 2006, 34 (Suppl. 1), D668−D672.
(22) MarvinSketch, version 5.8; ChemAxon: Budapest, Hungary,
2012; http://www.chemaxon.com.
(23) R Core Team. R: A Language and Environment for Statistical
Computing; R Foundation for Statistical Computing: Vienna, Austria,
2013; http://www.R-project.org/.
(24) Varma, M. V. S.; Feng, B.; Obach, R. S.; Troutman, M. D.;
Chupka, J.; Miller, H. R.; El-Kattan, A. Physicochemical Determinants
of Human Renal Clearance. J. Med. Chem. 2009, 52, 4844−4852.
(25) Testa, B/; Krämer, S. D. The Biochemistry of Drug
MetabolismAn Introduction, Part 2. Redox Reactions and Their
Enzymes. Chem. Biodiversity 2007, 4, 257−405.
(26) Guha, R.; Dexheimer, T. S.; Kestranek, A. N.; Jadhav, A.;
Chervenak, A. M.; Ford, M. G.; Simeonov, A.; Roth, G. P.; Thomas, C.
J. Exploratory Analysis of Kinetic Solubility Measurements of a Small
Molecule Library. Bioorg. Med. Chem. 2011, 19, 4127−4134.
(27) Varma, M. V.; Obach, R. S.; Rotter, C.; Miller, H. R.; Chang, G.;
Steyn, S. J.; El-Kattan, A.; Troutman, M. D. Physicochemical Space for
Optimum Oral Bioavailability: Contribution of Human Intestinal
Absorption and First-Pass Elimination. J. Med. Chem. 2010, 53 (3),
(28) Lombardo, F.; Obach, R. S.; Varma, M. V.; Stringer, R.;
Berellini, G. Clearance Mechanism Assignment and Total Clearance
Prediction in Human Based upon in Silico Models. J. Med. Chem.
2014, 57, 4397−4405.
(29) Rodgers, T.; Rowland, M. Physiologically Based Pharmacokinetic Modelling 2: Predicting the Tissue Distribution of Acids, Very
Weak Bases, Neutrals and Zwitterions. J. Pharm. Sci. 2006, 95, 1238−
(30) Yun, Y. E.; Edginton, A. N. Correlation-Based Prediction of
Tissue-to-Plasma Partition Coefficients Using Readily Available Input
Parameters. Xenobiotica 2013, 43, 839−852.
(31) Yokogawa, K.; Ishizaki, J.; Ohkuma, S.; Miyamoto, K. Influence
of Lipophilicity and Lysosomal Accumulation on Tissue Distribution
Kinetics of Basic Drugs: A Physiologically Based Pharmacokinetic
Model. Methods Find. Exp. Clin. Pharmacol. 2002, 24 (2), 81−93.
(32) Ndolo, R. A.; Luan, Y.; Duan, S.; Forrest, M. L.; Krise, J. P.
Lysosomotropic Properties of Weakly Basic Anticancer Agents
Promote Cancer Cell Selectivity in Vitro. PLoS One 2012, 7, e49366.
(33) Gerweck, L. E.; Seetharaman, K. Cellular pH Gradient in Tumor
versus Normal Tissue: Potential Exploitation for the Treatment of
Cancer. Cancer Res. 1996, 56 (6), 1194−1198.
(34) Poulin, P.; Dambach, D. M.; Hartley, D. H.; Ford, K.; Theil, F.P.; Harstad, E.; Halladay, J.; Choo, E.; Boggs, J.; Liederer, B. M.; Dean,
B.; Diaz, D. An Algorithm for Evaluating Potential Tissue Drug
Distribution in Toxicology Studies from Readily Available Pharmacokinetic Parameters. J. Pharm. Sci. 2013, 102, 3816−3829.
(35) Small, H.; Gardner, I.; Jones, H. M.; Davis, J.; Rowland, M.
Measurement of Binding of Basic Drugs to Acidic Phospholipids Using
Surface Plasmon Resonance and Incorporation of the Data into
Mechanistic Tissue Composition Equations To Predict Steady-State
Volume of Distribution. Drug Metab. Dispos. 2011, 39, 1789−1793.
(36) Ishizaki, J.; Yokogawa, K.; Nakashima, E.; Ohkuma, S.; Ichimura,
F. Uptake of Basic Drugs into Rat Lung Granule Fraction in Vitro.
Biol. Pharm. Bull. 1998, 21 (8), 858−861.
(37) Bingham, M.; Rankovic, Z. Medicinal Chemistry Challenges in
CNS Drug Discovery. In RSC Drug Discovery; Royal Society of
Chemistry: Cambridge, U.K., 2012; Chapter 18, pp 465−509.
(38) Yoshida, F.; Topliss, J. G. QSAR Model for Drug Human Oral
Bioavailability 1. J. Med. Chem. 2000, 43, 2575−2585.
(39) Peters, J.-U.; Schnider, P.; Mattei, P.; Kansy, M. Pharmacological Promiscuity: Dependence on Compound Properties and Target
Specificity in a Set of Recent Roche Compounds. ChemMedChem
2009, 4, 680−686.
(40) Karlgren, M.; Vildhede, A.; Norinder, U.; Wisniewski, J. R.;
Kimoto, E.; Lai, Y.; Haglund, U.; Artursson, P. Classification of
Inhibitors of Hepatic Organic Anion Transporting Polypeptides
(OATPs): Influence of Protein Expression on Drug−Drug Interactions. J. Med. Chem. 2012, 55 (10), 4740−4763.
(41) Stepan, A. F.; Walker, D. P.; Bauman, J.; Price, D. A.; Baillie, T.
A.; Kalgutkar, A. S.; Aleo, M. D. Structural Alert/Reactive Metabolite
Concept As Applied in Medicinal Chemistry To Mitigate the Risk of
Idiosyncratic Drug Toxicity: A Perspective Based on the Critical
Examination of Trends in the Top 200 Drugs Marketed in the United
States. Chem. Res. Toxicol. 2011, 24, 1345−1410.
(42) Leeson, P. D.; Springthorpe, B. The Influence of Drug-like
Concepts on Decision-Making in Medicinal Chemistry. Nat. Rev. Drug
Discovery 2007, 6, 881−890.
(43) Poulin, P.; Ekins, S.; Theil, F.-P. A Hybrid Approach to
Advancing Quantitative Prediction of Tissue Distribution of Basic
Drugs in Human. Toxicol. Appl. Pharmacol. 2011, 250, 194−212.
(44) Macintyre, A. C.; Cutler, D. J. The Potential Role of Lysosomes
in Tissue Distribution of Weak Bases. Biopharm. Drug Dispos. 1988, 9
(6), 513−526.
(45) Diaz, D.; Ford, K. A.; Hartley, D. P.; Harstad, E. B.; Cain, G. R.;
Achilles-Poon, K.; Nguyen, T.; Peng, J.; Zheng, Z.; Merchant, M.;
Sutherlin, D. P.; Gaudino, J. J.; Kaus, R.; Lewin-Koh, S. C.; Choo, E.
F.; Liederer, B. M.; Dambach, D. M. Pharmacokinetic Drivers of
Toxicity for Basic Molecules: Strategy to Lower pKa Results in
Decreased Tissue Exposure and Toxicity for a Small Molecule Met
Inhibitor. Toxicol. Appl. Pharmacol. 2013, 266, 86−94.
(46) Tengstrand, E. A.; Miwa, G. T.; Hsieh, F. Y. Bis(monoacylglycerol)phosphate as a Non-Invasive Biomarker To
Monitor the Onset and Time-Course of Phospholipidosis with
dx.doi.org/10.1021/jm501000a | J. Med. Chem. 2014, 57, 9701−9717
Journal of Medicinal Chemistry
Drug-Induced Toxicities. Expert Opin. Drug Metab. Toxicol. 2010, 6,
(47) Donato, M. T.; Gomez-Lechon, M. J. Drug-Induced Liver
Steatosis and Phospholipidosis: Cell-Based Assays for Early Screening
of Drug Candidates. Curr. Drug Metab. 2012, 13 (8), 1160−1173.
(48) Bernstein, P. R.; Ciaccio, P.; Morelli, J. Drug-Induced
Phospholipidosis, 1st ed.; Elsevier Inc.: Amsterdam, 2011; Vol. 46, pp
(49) Ishizaki, J.; Yokogawa, K.; Nakashima, E. M. I.; Ichimura, F.
Relationships between the Hepatic Intrinsic Clearance or Blood CellPlasma Partition Coefficient in the Rabbit and the Lipophilicity of
Basic Drugs. J. Pharm. Pharmacol. 1997, 49 (8), 768−772.
(50) Naven, R. T.; Swiss, R.; Klug-Mcleod, J.; Will, Y.; Greene, N.
The Development of Structure−Activity Relationships for Mitochondrial Dysfunction: Uncoupling of Oxidative Phosphorylation. Toxicol.
Sci. 2013, 131, 271−278.
(51) Ng, D. S.; Maickel, R. P.; Borowitz, J. L. Subcellular Distribution
of Weak Acids and Bases in Adrenal Medulla. Gen. Pharmacol. 1982,
13 (1), 15−20.
(52) Upthagrove, A. L.; Nelson, W. L. Importance of Amine pKa and
Distribution Coefficient in the Metabolism of Fluorinated Propranolol
Derivatives. Preparation, Identification of Metabolite Regioisomers,
and Metabolism by CYP2D6. Drug Metab. Dispos. 2001, 29 (11),
(53) Austin, R. P. The Binding of Drugs to Hepatocytes and Its
Relationship to Physicochemical Properties. Drug Metab. Dispos. 2004,
33, 419−425.
(54) Pedersen, J. M.; Matsson, P.; Bergstrom, C. A. S.; Hoogstraate,
J.; Noren, A.; LeCluyse, E. L.; Artursson, P. Early Identification of
Clinically Relevant Drug Interactions with the Human Bile Salt Export
Pump (BSEP/ABCB11). Toxicol. Sci. 2013, 136, 328−343.
(55) De Bruyn, T.; van Westen, G. J. P.; IJzerman, A. P.; Stieger, B.;
de Witte, P.; Augustijns, P. F.; Annaert, P. P. Structure-Based
Identification of OATP1B1/3 Inhibitors. Mol. Pharmacol. 2013, 83,
(56) Ahlin, G.; Karlsson, J.; Pedersen, J. M.; Gustavsson, L.; Larsson,
R.; Matsson, P.; Norinder, U.; Bergström, C. A. S.; Artursson, P.
Structural Requirements for Drug Inhibition of the Liver Specific
Human Organic Cation Transport Protein 1. J. Med. Chem. 2008, 51,
(57) Zolk, O.; Solbach, T. F.; König, J.; Fromm, M. F. Structural
Determinants of Inhibitor Interaction with the Human Organic Cation
Transporter OCT2 (SLC22A2). Naunyn-Schmiedeber’s Arch. Pharmacol. 2008, 379, 337−348.
(58) Bergstrom, M. A.; Isin, E. M.; Castagnoli, N.; Milne, C. E.
Bioactivation Pathways of the Cannabinoid Receptor 1 Antagonist
Rimonabant. Drug Metab. Dispos. 2011, 39, 1823−1832.
(59) Stachulski, A. V.; Meng, X. Glucuronides From Metabolites to
Medicines: A Survey of the in Vivo Generation, Chemical Synthesis
and Properties of Glucuronides. Nat. Prod. Rep. 2013, 30, 806.
(60) Dragovic, S.; Gunness, P.; Ingelman-Sundberg, M.; Vermeulen,
N. P. E.; Commandeur, J. N. M. Characterization of Human
Cytochrome P450s Involved in the Bioactivation of Clozapine. Drug
Metab. Dispos. 2013, 41, 651−658.
(61) Doss, G. A.; Miller, R. R.; Zhang, Z.; Teffera, Y.; Nargund, R. P.;
Palucki, B.; Park, M. K.; Tang, Y. S.; Evans, D. C.; Baillie, T. A.;
Stearns, R. A. Metabolic Activation of a 1,3-Disubstituted Piperazine
Derivative: Evidence for a Novel Ring Contraction to an Imidazoline.
Chem. Res. Toxicol. 2005, 18, 271−276.
(62) Stahly, G. P. Diversity in Single- and Multiple-Component
Crystals: The Search for and Prevalence of Polymorphs and
Cocrystals. Cryst. Growth Des. 2007, 7, 1007−1026.
(63) Childs, S. L.; Stahly, G. P.; Park, A. The Salt−Cocrystal
Continuum: The Influence of Crystal Structure on Ionization State.
Mol. Pharmaceutics 2007, 4, 323−338.
(64) Aakeröy, C. B.; Fasulo, M. E.; Desper, J. Cocrystal or Salt: Does
It Really Matter? Mol. Pharmaceutics 2007, 4, 317−322.
(65) Sttippler, E.; Kopp, S.; Dressman, J. B. Comparison of US
Pharmacopeia Simulated Intestinal Fluid TS (without Pancreatin) and
Phosphate Standard Buffer pH 6.8, TS of the International
Pharmacopoeia with Respect to Their Use in in Vitro Dissolution
Testing. Dissolution Technol. 2004, 11 (2), 6−11.
(66) Serajuddin, A. Salt Formation To Improve Drug Solubility. Adv.
Drug Delivery Rev. 2007, 59 (7), 603−616.
(67) Ravula, S. B.; Yu, J.; Tran, J. A.; Arellano, M.; Tucci, F. C.;
Moree, W. J.; Li, B.-F.; Petroski, R. E.; Wen, J.; Malany, S.; Hoare, S. R.
J.; Madan, A.; Crowe, P. D.; Beaton, G. Lead Optimization of 2(Piperidin-3-yl)-1H-benzimidazoles: Identification of 2-Morpholinand 2-Thiomorpholin-2-yl-1H-benzimidazoles as Selective and CNS
Penetrating H1-Antihistamines for Insomnia. Bioorg. Med. Chem. Lett.
2012, 22, 421−426.
(68) Morgenthaler, M.; Schweizer, E.; Hoffmann-Röder, A.; Benini,
F.; Martin, R. E.; Jaeschke, G.; Wagner, B.; Fischer, H.; Bendels, S.;
Zimmerli, D.; Schneider, J.; Diederich, F.; Kansy, M.; Müller, K.
Predicting and Tuning Physicochemical Properties in Lead Optimization: Amine Basicities. ChemMedChem 2007, 2, 1100−1115.
(69) Fish, L. R.; Gilligan, M. T.; Humphries, A. C.; Ivarsson, M.;
Ladduwahetty, T.; Merchant, K. J.; O’Connor, D.; Patel, S.; Philipps,
E.; Vargas, H. M.; Hutson, P. H.; MacLeod, A. M. 4-Fluorosulfonylpiperidines: Selective 5-HT2A Ligands for the Treatment of
Insomnia. Bioorg. Med. Chem. Lett. 2005, 15, 3665−3669.
(70) Meloun, M.; Bordovská, S. Benchmarking and Validating
Algorithms That Estimate pKa Values of Drugs Based on Their
Molecular Structures. Anal. Bioanal. Chem. 2007, 389, 1267−1281.
(71) Liao, C.; Nicklaus, M. C. Comparison of Nine Programs
Predicting pKa Values of Pharmaceutical Substances. J. Chem. Inf.
Model. 2009, 49, 2801−2812.
(72) Settimo, L.; Bellman, K.; Knegtel, R. M. A. Comparison of the
Accuracy of Experimental and Predicted pKa Values of Basic and
Acidic Compounds. Pharm. Res. 2013, 31, 1082−1095.
(73) Cruciani, G.; Milletti, F.; Storchi, L.; Sforna, G.; Goracci, L. In
Silico pKa Prediction and ADME Profiling. Chem. Biodiversity 2009, 6,
(74) Kang, B. H.; Altieri, D. C. Compartmentalized Cancer Drug
Discovery Targeting Mitochondrial Hsp90 Chaperones. Oncogene
2009, 28, 3681−3688.
(75) Ling, D.; Park, W.; Park, S.-J.; Lu, Y.; Kim, K. S.; Hackett, M. J.;
Kim, B. H.; Yim, H.; Jeon, Y. S.; Na, K.; Hyeon, T. Multifunctional
Tumor pH-Sensitive Self-Assembled Nanoparticles for Bimodal
Imaging and Treatment of Resistant Heterogeneous Tumors. J. Am.
Chem. Soc. 2014, 136, 5647−5655.
(76) Aspiotis, R.; Chen, A.; Cauchon, E.; Dubé, D.; Falgueyret, J.-P.;
Gagné, S.; Gallant, M.; Grimm, E. L.; Houle, R.; Juteau, H.; Lacombe,
P.; Laliberté, S.; Lévesque, J.-F.; MacDonald, D.; McKay, D.; Percival,
M. D.; Roy, P.; Soisson, S. M.; Wu, T. The Discovery and Synthesis of
Potent Zwitterionic Inhibitors of Renin. Bioorg. Med. Chem. Lett. 2011,
21, 2430−2436.
(77) Peters, J.-U.; Lübbers, T.; Alanine, A.; Kolczewski, S.; Blasco, F.;
Steward, L. Cyclic Guanidines as Dual 5-HT5A/5-HT7 Receptor
Ligands: Optimising Brain Penetration. Bioorg. Med. Chem. Lett. 2008,
18, 262−266.
(78) Arbuckle, W.; Baugh, M.; Belshaw, S.; Bennett, D. J.; Bruin, J.;
Cai, J.; Cameron, K. S.; Claxton, C.; Dempster, M.; Everett, K.;
Fradera, X.; Hamilton, W.; Jones, P. S.; Kinghorn, E.; Long, C.;
Martin, I.; Robinson, J.; Westwood, P. 1H-Imidazo[4,5-c]pyridine-4carbonitrile as Cathepsin S Inhibitors: Separation of Desired Cellular
Activity from Undesired Tissue Accumulation through Optimization
of Basic Nitrogen pKa. Bioorg. Med. Chem. Lett. 2011, 21, 932−935.
(79) Furber, M.; Tiden, A.-K.; Gardiner, P.; Mete, A.; Ford, R.;
Millichip, I.; Stein, L.; Mather, A.; Kinchin, E.; Luckhurst, C.; Barber,
S.; Cage, P.; Sanganee, H.; Austin, R.; Chohan, K.; Beri, R.; Thong, B.;
Wallace, A.; Oreffo, V.; Hutchinson, R.; Harper, S.; Debreczeni, J.;
Breed, J.; Wissler, L.; Edman, K. Cathepsin C Inhibitors: Property
Optimization and Identification of a Clinical Candidate. J. Med. Chem.
2014, 57, 2357−2367.
(80) Clark, M. P.; Ledeboer, M. W.; Davies, I.; Byrn, R. A.; Jones, S.
M.; Perola, E.; Tsai, A.; Jacobs, M.; Nti-Addae, K.; Bandrage, U. K.;
Boyd, M. J.; Bethiel, R. S.; Court, J. J.; Deng, H.; Duffy, J. P.; Dorsch,
dx.doi.org/10.1021/jm501000a | J. Med. Chem. 2014, 57, 9701−9717
Journal of Medicinal Chemistry
W. A.; Farmer, L. J.; Gao, H.; Gu, W.; Jackson, K.; Jacobs, D. H.;
Kennedy, J. M.; Ledford, B.; Liang, J.; Maltais, F.; Murcko, M.; Wang,
T.; Wannamaker, M. W.; Bennett, H. B.; Leeman, J. R.; McNeil, C.;
Taylor, W. P.; Memmott, C.; Jiang, M.; Rijnbrand, R.; Bral, C.;
Germann, U.; Nezami, A.; Zhang, Y.; Salituro, F. G.; Bennani, Y. L.;
Charifson, P. S. The Discovery of VX-787: A Novel, First-in-Class,
Orally Bioavailable Azaindole Inhibitor of Influenza PB2. J. Med. Chem.
2014, 57 (15), 6668−6678.
(81) Sweetana, S.; Akers, M. J. Solubility Principles and Practices for
Parenteral Drug Dosage Form Development. PDA J. Pharm. Sci.
Technol. 1996, 50 (5), 330−342.
(82) Kell, D. B.; Dobson, P. D.; Oliver, S. G. Pharmaceutical Drug
Transport: The Issues and the Implications That It Is Essentially
Carrier-Mediated Only. Drug Discovery Today 2011, 16 (15), 704−
(83) Data from all analyses presented in this paper are available free
of charge via the Internet at https://github.com/PatWalters/acidbase.
dx.doi.org/10.1021/jm501000a | J. Med. Chem. 2014, 57, 9701−9717