Exploring Molecular Promiscuity from a Ligand and Target Perspective

Chapter 3

Exploring Molecular Promiscuity from a Ligand and Target Perspective Downloaded by PURDUE UNIV on November 25, 2016 | http://pubs.acs.org Publication Date (Web): October 5, 2016 | doi: 10.1021/bk-2016-1222.ch003

Ye Hu and Jürgen Bajorath* Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany *E-mail: [email protected]

Polypharmacology is an emerging theme in drug discovery. There is increasing evidence that many pharmaceutically relevant compounds elicit their therapeutic effects by acting on multiple biological targets. In this context, promiscuity is defined as the ability of compounds to specifically interact with different targets (as opposed to non-specific interactions) and hence provides the molecular basis of polypharmacology. There is much debate in the scientific community concerning the degree of promiscuity and polypharmacology among bioactive compounds and drugs. The only way to assess compound promiscuity -beyond speculation- is to focus on currently available activity data, even though the picture one obtains is likely incomplete. However, assessing promiscuity through data mining yields meaningful estimates because currently available sample sizes of compound activity data are so large that statistically sound trends can be derived from their analysis. We systematically determine promiscuity rates taking data confidence criteria into account and follow promiscuity on a time course. In addition, we demonstrate that promiscuity can be viewed from a ligand and target perspective on the basis of compound activity data.

© 2016 American Chemical Society Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.

Downloaded by PURDUE UNIV on November 25, 2016 | http://pubs.acs.org Publication Date (Web): October 5, 2016 | doi: 10.1021/bk-2016-1222.ch003

Introduction In the context of polypharmacology (1–6), compound promiscuity is defined as the ability of small molecules to specifically interact with multiple targets (1–4). So-defined promiscuity is distinct from undesirable non-specific interactions that give rise to assay liabilities (7) and disqualify compounds for use as therapeutic agents. Increasing notion of polypharmacology conflicts with the compound specificity paradigm that has guided drug discovery efforts for at least three decades (during the ‘reductionist’ era of drug discovery). It is thus not surprising that there is much debate -and speculation- in the scientific community how promiscuous drugs might really be and to what extent their pharmacological effects might indeed be determined by interactions with multiple targets. Dissecting pharmacological and functional effects in a systematic manner is a non-trivial task. Typically, compound functions are explored on a case-by-case basis. However, compound promiscuity as the molecular basis of polypharmacology can be assessed in compound profiling experiments (i.e., by testing compound libraries on arrays of targets) and, on a larger scale, through mining of compound activity data. The latter approach essentially provides the only opportunity to systematically determine the current degree of promiscuity for bioactive compounds and drugs. Although data-driven assessments are more desirable than assumptions or educated guesses, it is often argued that so-determined promiscuity rates might be too low because ‘not all compounds have been tested against all targets’. This conjecture refers to the well-known issue of data incompleteness (8), which also affects chemogenomics (9), given its elusive ultimate goal to ‘test all compounds against all targets’ (9). While it is highly unlikely that all small molecules will ever be tested in a consistent manner against all genomic targets, it is often not considered that current volumes of compound activity data are already so large that it is possible to derive statistically relevant activity or promiscuity trends from these data (1, 10). For example, the current release 20 of the ChEMBL (11, 12) database (ChEMBL20), the major public repository of compound and activity data from medicinal chemistry, contains nearly 1.5 million compounds with known activity against nearly 11,000 diverse biological targets and a total of more than 13 million activity records. In addition, PubChem (13, 14), the major public source of biological screening data, currently contains more than 60 million compound entries, 1.1 million assays/screens, and nearly 207,000 confirmatory assays (re-evaluating compound activity annotations from primary screens). Hence, although the data incompleteness argument will likely always apply, to a more or lesser extent, it should be possible to extract meaningful promiscuity estimates from current activity data, provided the data are analyzed in a careful and consistent manner. In the following, we report the results of systematic compound promiscuity analyses.

Influence of Data Confidence Criteria on Promiscuity Assessment One of the most critical aspects of data-driven compound promiscuity analysis is that data confidence criteria must be carefully considered (15). For example, 20 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


ChEMBL provides a variety of data selection criteria that can be sequentially applied to refine data selection and gradually increase data confidence levels, as illustrated in Figure 1.

Figure 1. Shown is a workflow to refine activity data selection and increase data confidence levels through sequential application of selection criteria implemented in ChEMBL. In addition, the corresponding compound statistics for ChEMBL release 18 (ChEMBL18) is reported. Each selection step in Figure 1 defines a compound subset (sets 1-8) and along the sequential selection path, data confidence gradually increases. In the first step, all compounds with available target activity annotations were taken from ChEMBL18 (set 1) and in the second step, all compounds active against human targets were assembled (set 2). In the third step, direct binding/inhibition assays with the highest confidence level were selected (set 3) and in step 4, single protein targets were specified. Steps 5-7 defined activity measurements and units and step 8 removed activity records with ambiguous annotations (set 8). This sequence reduced the total number of active compounds from 1,291,676 (set 1) to 148,373 (set 8). 21

Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


Special attention should be given to sets 2, 3, and 8 that represented difference confidence levels, as further discussed below. Set 2 was a low-confidence set because it contained all compounds reported to be active against human targets without considering any assay conditions or the stringency of activity measurements. Set 3 represented a medium-confidence set since it comprised all compounds active against human targets on the basis of direct binding assays with highest assay confidence score. Set 8 (148,373 compounds) represented the set with highest data confidence because it additionally required well-defined and clearly specified activity measurements. Figure 2 illustrates the influence of data confidence levels on the global degree of promiscuity averaged over all compounds available in corresponding sets from ChEMBL18. The degree of promiscuity decreased with increasing data confidence levels from 6.7 targets per compound when all available activity annotations were considered to only 1.5 for the high-confidence set (15). Thus, depending on activity data confidence levels, different conclusion would be drawn from promiscuity analysis.

Figure 2. The average degree of promiscuity for bioactive compounds in sets 1-8 from ChEMBL18 according to Figure 1 is reported.

Promiscuity on a Time Course Monitoring promiscuity over time while compound activity data grow further refines our view of promiscuity. Figure 3 shows the progression of the average degree of promiscuity of bioactive compounds over time from 1976 to 2014. 22 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


Compounds from ChEMBL20 were assigned to individual years on the basis of the date they were reported first. For each year, cumulative data sets were then calculated according to sets 2, 3, and 8 confidence criteria and analyzed (16).

Figure 3. The average degree of promiscuity of bioactive compounds is monitored over time (1976-2014) for cumulative data sets generated on the basis of low-, medium-, and high-confidence activity data from ChEMBL20.

For low- and medium-confidence data, a gradual increase in the average degree of promiscuity from 1.1 and 1.0 in 1976 to 2.5 and 2.1 in 2014 was observed, respectively. For the high-confidence data set, promiscuity increased from 1.0 to 1.5. The promiscuity degree of 1.5 targets per compound was reached in 2001 and remained constant until 2014, despite the massive growth of compound activity data over the past decade. Taken together, these findings indicated that the average promiscuity of bioactive compounds was lower than frequently thought. An analogous time course analysis was carried out for approved drugs (17). Drugs were taken from DrugBank 4.0 (18) and mapped to ChEMBL20. Average drug promiscuity was then monitored from 2000 to 2014, as reported in Figure 4. Clear differences between bioactive compounds and drugs were observed. For high-confidence activity data, an increase in average promiscuity from 1.9 targets per drug to 3.7 was detected. For medium- and low-confidence data, the increase was 3.3 to 15.9 and 5.9 to 24.4 targets per drug, respectively. Thus, drugs displayed on average a notably higher degree of promiscuity than bioactive compounds and, in this case, differences between data sets with varying confidence levels were much larger. 23



Figure 4. The average degree of promiscuity of approved drugs is monitored over time (2000-2014) according to Figure 3.

The strong influence of data confidence on apparent drug promiscuity is highlighted using the marketed kinase inhibitor imatinib as an example, which is used as a drug for cancer treatment (Gleevec®). On the basis of its characterization and therapeutic use, imatinib is expected to be a highly promiscuous compound. In fact, it belongs to the group of clinical kinase inhibitors that have much contributed to the popularity of the polypharmacology paradigm. Promiscuity of imatinib on a time course is shown in Figure 5. The results are striking. On the basis of low- and medium-confidence activity data, a strong increase of promiscuity was observed for imatinib beginning in 2004, ultimately leading to a promiscuity degree of 690 and 406 in 2014, respectively. Such high degrees of apparent promiscuity are difficult to rationalize. On the basis of high-confidence data, a similarly strong increase was not detectable and the 2014 promiscuity degree of imatinib was 27, which is more realistic. The obvious question why drugs have on average higher promiscuity than bioactive compounds (forming the pool from which drugs originate) cannot be answered with certainty at present. It is possible that drug candidates and drugs are more extensively tested than active compounds, an explanation referring to data incompleteness. It is also possible that promiscuous compounds, in the absence of substantial safety issues, are preferentially selected for efficacy during pre-clinical and clinical evaluation, likely leading to an enrichment of promiscuous compounds during clinical trials. In any event, the time-dependent promiscuity profiles of bioactive compounds and drugs determined on the basis of high-confidence data indicated that the promiscuity trends were quite stable. 24



Figure 5. The degree of promiscuity of the kinase inhibitor imatinib is monitored over time (2000-2014) according to Figure 3.

Active versus Inactive Compounds Repositories for active compounds such as ChEMBL do not contain confirmed inactive compounds (which are usually not reported in the literature). Also, ChEMBL does not contain information against how many targets an active compound might have been tested. It is therefore not possible to relate promiscuity to the number of instances a compound has been assayed. This point is frequently raised when discussing promiscuity and also related to the issue of data incompleteness. However, screening data deposited in PubChem make it possible to take assay frequency and confirmed inactive compounds into account. For example, the majority of active compounds from PubChem confirmatory assays have been tested in more than 50 different assays (19). The degree of promiscuity determined for these PubChem compounds was 2.5 (19), thus readily comparable to the average promiscuity of ChEMBL compounds based on medium- or low-confidence data, as discussed above. These findings also indicate that current promiscuity degrees derived from activity data are likely to represent meaningful estimates for bioactive compounds.

Promiscuity across Molecular Property Ranges Promiscuity of bioactive compounds was also determined across molecular weight and logP ranges (logP was used as a measure of lipophilicity) (20) on the basis of high-confidence activity data (set 8 criteria) from ChEMBL20, as reported in Figures 6 and 7, respectively. 25 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


Figure 6. Average promiscuity degrees for bioactive compounds of increasing molecular weight are determined on the basis of high-confidence activity data (set 8) from ChEMBL20. The red horizontal line marks the average degree of promiscuity for bioactive compounds (1.5) according to Figure 3.

Figure 7. Average promiscuity degrees for bioactive compounds with increasing logP values (lipophilicity) are determined on the basis of high-confidence activity data (set 8) from ChEMBL20. The red horizontal line marks the average degree of promiscuity (1.5).

Figure 6 shows that promiscuity of bioactive compounds was essentially constant at the 1.5 level over different molecular weight ranges. A slight increase in average promiscuity was only observed for very small compounds up to a weight of 300 Da. For the smallest compounds with a weight ≤ 200 Da (fragments), the average promiscuity degree was 2.1. These findings were intuitive since fragments typically have a higher propensity to be accommodated in different binding sites than larger compounds. 26 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


Similar yet partly surprising observations were made when promiscuity of bioactive compounds was monitored over increasing logP ranges, as shown in Figure 7. Also in this case, most promiscuity degrees were close to the global average of 1.5. Although one might expect that lipophilic compounds should have an increased tendency of promiscuity, this expectation was not supported by promiscuity calculations on the basis of high-confidence activity data. If at all, promiscuity degrees were slightly increased above the global average for hydrophilic compounds within the logP range of -2 to +2, whereas no increase was detected for compounds falling into higher logP value ranges. Thus, a general increase in promiscuity for lipophilic compounds was not observed.

Promiscuity across Popular Target Families Promiscuity was also assessed for compounds active against prominent therapeutic target families (20) including G protein coupled receptors (GPCRs), ion channels, kinases, nuclear receptors, and proteases, as reported in Figure 8.

Figure 8. Average promiscuity degrees of compounds active against selected target families are reported. The red horizontal line marks the average degree of promiscuity (1.5). Surprising observations were also made in this case. For all target families, the average promiscuity of ligands was close to the global average of 1.5; only for proteases, a slight increase (1.7) was observed. The results were especially interesting for kinase inhibitors that are often thought to be particularly promiscuous (with imatinib being a prominent example, as discussed above). However, the analysis did not support this view (although many kinase inhibitors used in cancer therapy are known to be promiscuous). Rather, kinase inhibitors were overall not distinct from other bioactive compounds in their degree of promiscuity. The kinase inhibitor set contained a total of 22,254 compounds with activity against 278 kinases (i.e., more than half of the kinome) and was hence 27



a relevant sample for statistical analysis. It should also be considered that many kinase inhibitors have been experimentally profiled against panels of kinases spanning the kinome. Hence, the results of our analysis can certainly not be disregarded on the basis of assumed data sparseness. Rather, they suggest that not all kinase inhibitors display above average promiscuity; in fact, the majority of kinase inhibitors is currently only annotated with a single kinase activity (21). As illustrated for imatinib, focusing on high-confidence activity data probably plays an important role in this case, preventing an inflation of kinase annotations for subsets of inhibitors, many of which are probably artificial in nature, due to low-confidence activity readouts.

Lessons Learned from Ligand-Centric Promiscuity Analysis Taken together, the results of rigorous data-driven analysis of compound promiscuity balance general views and expectations about the extent of promiscuity across bioactive compounds, especially for ligands of prominent target families. Although expectations that many therapeutically active compounds should be highly promiscuous might often be intuitive within the conceptual framework of polypharmacology, it must be carefully considered that currently available data do not generally support such expectations. We have also provided support for the view that current findings of data-driven promiscuity analysis cannot simply be attributed to data incompleteness, given their consistency and the size of data samples from which they were obtained. It is evident from data analysis, however, that drugs have on average a significantly higher degree of promiscuity than bioactive compounds. It should be noted that calculated average promiscuity values for drugs might be skewed by subsets of highly promiscuous drugs (such as imatinib), as indicated by previous findings that median promiscuity values for drug sets are generally lower than average values (20). Nonetheless, drugs display the tendency to be more promiscuous than bioactive compounds, as clearly indicated by monitoring drug and compound promiscuity on a time course.

Target-Centric View of Promiscuity The studies reported above have focused on evaluating the promiscuity of small molecules. However, promiscuity can also be rationalized from a target perspective. Target proteins typically have different abilities to interact with small molecules, given the particular architectures and chemical characteristics of their active or binding sites. There are notoriously ‘good’ and ‘bad’ (difficult) small molecule targets such as many cytosolic enzymes participating in metabolic pathways and cell surface receptors engaging in protein-protein interactions, respectively. Moreover, among targets that bind small molecules, differences in the structural diversity of ligands can be detected. From a target perspective, promiscuity can be rationalized as the ability of a target to interact with structurally diverse compounds (belonging to different classes). Target promiscuity has been explored by analyzing and comparing binding site features and by relating 28 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


such features (qualitatively or quantitatively) to ligand binding characteristics. However, there currently are only limited insights into the structural origins of target promiscuity. We have reasoned that target promiscuity could also be evaluated using compound activity data (22). The conceptual basis for this approach was provided by de-convoluting biologically relevant space into ‘scaffold’ (molecular framework) and ‘activity’ spaces, as illustrated in Figure 9. Compounds containing the same scaffold represent a series of analogs with a unique core structure. Therefore, bioactive compounds are reduced to scaffolds and scaffold diversity can be effectively used as a measure of structural diversity. In addition, activity space comprises all activity annotations of bioactive compounds.

Figure 9. The concept of de-convoluting biologically relevant chemical space into scaffold space and activity space to evaluate target promiscuity on the basis of compound activity data is illustrated.

Following this concept, two ‘Target Promiscuity Indices’ (TPIs) were defined as follows: First-order target promiscuity index (TPI_1) provides the number of unique scaffolds isolated from all compounds active against the target. Accordingly, TPI_1 indicates the ability of a target to specifically interact with structurally diverse compounds. Second-order target promiscuity index (TPI_2) reports the average degree of promiscuity of all compounds active against the target. Therefore, TPI_2 reflects the tendency of a target to interact with specific or promiscuous compounds. Figures 10 and 11 report the distribution of TPI_1 and TPI_2, respectively, over all targets from ChEMBL20 for which high-confidence activity data were available. Compounds with available assay-dependent IC50 values or assay-independent equilibrium constants (Ki values) were separately analyzed. 29



Figure 10. The distribution of TPI_1 values is reported over all targets from ChEMBL20 for which high-confidence activity data were available.

Figure 11. The distribution of TPI_2 values is reported over all targets from ChEMBL20 for which high-confidence activity data were available. From the distribution in Figure 10, average TPI_1 values of 61 and 77 were calculated on the basis of IC50 and Ki data, respectively. Thus, many targets were found to interact with structurally diverse compounds; an indicator of target promiscuity. Furthermore, from the distribution of TPI_2 values in Figure 11, it 30 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


was determined that only 18% of all targets interacted with compounds having no other reported activity. Thus, most targets bound promiscuous compounds; another indicator of target promiscuity. From these findings, it was possible to draw the interesting conclusion that target promiscuity, as assessed herein, was generally high, whereas compound promiscuity was generally low, as discussed above. Moreover, by relating TPI_1 and TPI_2 values to each other, targets with characteristic TPI patterns were identified. Examples are provided in Table 1.

Table 1. Exemplary targets with distinct TPI patterns are listed. ‘%Prom-Cpds’ gives the percentage of promiscuous compounds within the ligand set of each target from ChEMBL20 on the basis of IC50 data. TPI pattern High TPI_1 Low TPI_2

Low TPI_1 High TPI_2

Target name

#Cpds

TPI_1

TPI_2

%Prom-Cpds

Leukotriene A4 hydrolase

217

124

1.01

1.4%

C-X-C chemokine receptor type 3

372

129

1.00

0%

Group IID secretory phospholipase A2

10

4

4.70

90.0%

Matrix metalloproteinase 16

12

6

6.42

91.7%

The first two exemplary targets listed in Table 1 were characterized by high TPI_1 and low TPI_2 values. Thus, these targets recognized many structurally diverse compounds with no or very few other reported activities. For example, for the C-X-C chemokine receptor type 3, a total of 372 active compounds were known. Its TPI_1 value of 129 means that this receptor recognized compounds with 129 distinct scaffolds (reflecting a high degree of structural diversity). Its TPI_2 value of 1.0 means that none of these compounds had any other reported activity (hence, its ligand set did not contain any promiscuous compounds). The third and fourth target in Table 1 were characterized by an opposite TPI pattern, i.e., by low TPI_1 and high TPI_2 values. Thus, these targets (belonging to well-known families of small molecular targets) did not interact with many structural diverse compounds (which perhaps also explains the small number of compounds known to be active against these targets), but preferentially with promiscuous compounds. For example, group IID secretory phospholipase A2 had 10 known inhibitors represented by four scaffolds (corresponding to a TPI_1 value of 4), and nine of these inhibitors were promiscuous (yielding a TPI_2 value of 4.70). These examples illustrate that the analysis of TPI patterns might lead to a further differentiated picture of target promiscuity. Moreover, on the basis of TPI_2 values, promiscuity profiles of target families can also be generated. For this purpose, TPI_2 values were calculated for all members of a target family 31



and classified according to value ranges, as illustrated in Figure 12, which shows examples of related target families with varying promiscuity profiles including GPCRs and kinases. Such promiscuity profiles are helpful, for example, to identify target families, or subfamilies, which are promising candidates for the design of polypharmacological ligands (i.e., compounds with multi-target activities). To these ends, promiscuity profiles can be further refined by differentiating between intra- and inter-family multi-target activities. On the other hand, promiscuity profiles can also be used to prioritize target families with a notable tendency to interact with selective ligands.

Figure 12. Target profiles for related protein families are shown in a pie chart representation. In summary, on the basis of compound activity data, promiscuity can be analyzed in a ligand- and target-centric manner, which provides complementary views of promiscuity patterns.

Acknowledgments The authors are grateful to Swarit Jasial for his contribution to the study of compound promiscuity progression over time.

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Exploring Molecular Promiscuity from a Ligand and Target Perspective

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