Acidic and Basic Drugs in Medicinal Chemistry: A Perspective

Perspective pubs.acs.org/jmc

Acidic and Basic Drugs in Medicinal Chemistry: A Perspective Paul S. Charifson*,† and W. Patrick Walters†

J. Med. Chem. 2014.57:9701-9717. Downloaded from pubs.acs.org by UNIV OF SOUTH DAKOTA on 09/11/18. For personal use only.

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.

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INTRODUCTION 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 9701

dx.doi.org/10.1021/jm501000a | J. Med. Chem. 2014, 57, 9701−9717

Journal of Medicinal Chemistry

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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 database.

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 experimentally.

[ionized] [un‐ionized]

Conversely, for a weak base: pH − pK a = log

⎛ [solute] ⎞ octanol ⎟ log Poct/wat = log⎜⎜ un‐ionized ⎟ ⎝ [solute]water ⎠

[un‐ionized] [ionized]

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 species.17 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

⎛ ⎞ [solute]octanol ⎟ log Doct/wat = log⎜⎜ ionized 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 ionization. 9702



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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. 9703



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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, 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).

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METHODS

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. 9704



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Table 1. Literature Summary: Effects of Ionization State on DMPK Properties parameter solubility

permeability/P-gp efflux

oral absorption/ bioavailability

clearance

volume of distribution

protein binding

tissue distribution

metabolism

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 discussion). - Ionized molecules at physiological pH tend to interact with negatively charged lipid membranes generally resulting in a low permeability: neutrals > bases > zwitterions > acids. - Increasing lipophilicity may increase permeability, especially for acids, bases, and zwitterions. - 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 factor. - 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. 9705

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 parameter


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 molecules. - 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, 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, version 3.0.2.23

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DISTRIBUTION OF DRUGS ACROSS TARGET CLASSES AND THERAPEUTIC AREAS The fact that each assay in the ChEMBL database is annotated with a drug target class (enzyme, membrane receptor, ion 9706



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Table 2. Literature Summary: Effects of Ionization State on Safety Properties parameter selectivity

cellular distribution

transporter inhibition

reactive metabolites


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 pancreas. - 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 intermediate.

1, 6, 14, 39, 42

1, 31, 34, 43−51

54−57

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,

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IMPACT OF ACIDITY OR BASICITY ON DMPK PROPERTIES 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 9707



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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 acidic compounds) but also highlight an important point, i.e., that there are still a significant number of acids with potent cellular activity.

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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.

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pKa OPTIMIZATION 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)

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FORM/FORMULATABILITY 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 9712



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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.

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GENERAL OBSERVATIONS/SUMMARY 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 following: • 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 distribution. 9713



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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.

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ACKNOWLEDGMENTS The authors thank Jeremy Green and Jim Empfield for a thorough critique of this work as well as providing insightful suggestions.

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ABBREVIATIONS USED 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

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 617-341-6442. Author Contributions †

Both authors contributed equally to this work.

Notes

The authors declare no competing financial interest. Biographies 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 9714



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