Monitoring drug promiscuity over time

Data collection

From ChEMBL (release 18)⁴, compounds with direct interactions (i.e., assay relationship type “D”) with human targets at the highest confidence level (i.e., assay confidence score 9) were collected. The two ChEMBL parameters ‘assay relationship type’ and ‘assay confidence score’ qualify and quantify the level of confidence that the activity against a given target is evaluated in a relevant assay system, respectively. Accordingly, type “D” and score 9 represent the highest level of confidence for activity data. In addition, two types of activity measurements were considered including assay-independent equilibrium constants (K_i values) and assay-dependent IC₅₀ values. To ensure a high level of data integrity, only compounds with explicitly defined K_i or IC₅₀ values were selected. Hence, approximate measurements such as “>”, “<”, and “~” were disregarded. Compounds with multiple K_i or IC₅₀ measurements for the same target were retained if all these values fell within the same order of magnitude. Otherwise, the target activity was omitted from further consideration. Structures of all qualifying bioactive compounds were standardized using the Molecular Operating Environment (MOE)¹³ and transformed into canonical SMILES strings¹⁴. The so assembled compound set exclusively utilized high-confidence activity data (high-confidence data set).

Approved small molecule drugs with available structure and activity information were collected from the latest release of DrugBank (version 4.1)⁶. To synchronize the activity analysis in ChEMBL and DrugBank, all reported ‘drug action’ targets, metabolizing enzymes, transporters, and carriers were assembled for approved drugs. In some instances, drug target activity might refer to a group of related proteins. For example, atomoxetine was annotated with N-methyl-D-aspartate (NMDA) receptor including seven subtypes. Accordingly, seven UniProt¹⁵ accession IDs (UniProtIDs) were associated with NMDA receptor. Thus, the maximal number of target annotations was collected for approved drugs on the basis of UniProtIDs. Drug structures were also standardized using MOE and transformed into canonical SMILES strings.

Monitoring drug activity records over time

Most compound activity data in ChEMBL are extracted from medicinal chemistry literature and patent sources⁴. Therefore, the release dates of activity data are frequently recorded in this database. However, DrugBank does not report dates for individual target annotations. To systematically monitor drug promiscuity over time, all approved drugs from DrugBank were mapped to ChEMBL by comparing canonical SMILES strings. If a drug (D) and a bioactive compound (B) shared the same SMILES string, a match was obtained. It should be noted that the name of a drug in DrugBank and ChEMBL might differ (i.e., matching by drug/compound name is not reliable). For each match, activity data release dates of compound B were recorded and assigned to drug D. Each activity record represented a target annotation (the terms target activity and target annotation are synonymously used). For instance, if compound B was reported to be active against target I in 2001, target II in 2005, and target III in 2009, the cumulative activity records for drug D consisted of target I in 2001, targets I and II in 2005, and targets I, II, and III in 2009. Thus, the promiscuity rate of D increased over time from 1 to 3. All activity records were organized into 14 time intervals, as illustrated in Figure 1. All activity records reported before 2000 were assigned to 2000, the starting point of our analysis, and all activity data released after 2012 were assigned to the last period “>2012”. For each time interval, the cumulative activity profile was recorded. Hence, changes in the promiscuity rate of a drug were successively determined over the years. Cumulative activity profiles were compared to target annotations available in DrugBank.

Figure 1. Organization of activity records.

The organization of the activity records for a drug over different years is schematically illustrated. Drug D and a bioactive compound B share the same SMILES string (D is mapped to B). The activity records of compound B are extracted from ChEMBL. B is reported to be active against target I in 2001, II in 2005, and III in 2009. These activity records are then assigned to drug D and organized into 14 time intervals (12 of which represent individual years, except 2000 (see text) and >2012). For each interval, a cumulative activity profile is generated for D and recorded. The total number of activity annotations is given in red.

Low-confidence data sets

In order to investigate the effect of activity data confidence levels on drug promiscuity, two data sets with lower confidence were assembled from ChEMBL (release 18). For the generation of low-confidence data sets, two criteria that influence the compound data integrity, i.e., the confidence level of activity and the type of activity measurements were disregarded in subsequent steps. In low-confidence set 1, the criterion of activity measurement type was not considered. Hence, in addition to K_i and IC₅₀ values, all other potency annotations were equally considered (including “%max”, “Efficacy”, “EC₅₀”, “K_d”, and “Residual Activity”) for all compounds with ‘direct interactions’ with human targets and assay confidence score 9. In addition, the consistency and quality of potency measurements was not considered. In low-confidence set 2, the confidence level of activity (assay relationship type and assay confidence score) was not considered, in addition to the type of activity measurements. Therefore, the stringency of activity data and compound selection decreased from the high-confidence set over low-confidence set 1 to low-confidence set 2.

Progression of drug promiscuity over time was systematically evaluated on the basis of all three data sets.

Bioactive compounds and approved drugs

On the basis of the selection criteria described above, a total of 143,424 bioactive compounds with high-confidence activity data were obtained from ChEMBL. These compounds were active against 1376 different targets and yielded 219,602 compound-target interactions, as reported in Table 1. Furthermore, from DrugBank 4.1, 1429 approved drugs were obtained that were annotated with 1657 target proteins corresponding to 10,679 drug-target interactions (Table 1). Thus, there were nearly 100 times more bioactive compounds than approved drugs. However, with 1657 targets, drugs covered a larger target space than bioactive compounds (1376 targets). On average, a bioactive compound was active against 1.5 targets, whereas an approved drug was annotated with 7.5 targets. Compared to a recent analysis of promiscuity rates¹¹, which also included a previous release of DrugBank, the average promiscuity rate of approved drugs further increased from 5.9 to 7.5, while the degree of promiscuity among bioactive compounds remained essentially constant.

To monitor drug promiscuity over time, all approved drugs were mapped to bioactive compounds in ChEMBL for which release dates of activity records were reported (as detailed in the Methods section). For 518 of the 1429 approved drugs taken from DrugBank, high-confidence activity data released over different years were found in ChEMBL. These 518 drugs provided the basis for our time-dependent promiscuity analysis.

Data inconsistency

For the 518 qualifying drugs, we first compared their target annotations in DrugBank and the total number of targets derived from high-confidence activity records in ChEMBL. As reported in Figure 2a, most of the drugs had different numbers of targets in the two databases. Only 32 drugs (~6%) were found to have the same number of target annotations in DrugBank and ChEMBL. The total number of target annotations of a drug represented its promiscuity rate. A total of 439 drugs had higher promiscuity rates in DrugBank than in ChEMBL. Opposite observations were only made for 47 drugs. On average, the 518 drugs were annotated with ~10.1 targets in DrugBank and ~3.2 targets derived from high-confidence ChEMBL activity records. Hence, promiscuity rates in DrugBank were much higher than in ChEMBL. Exemplary drugs having the same or different degrees of promiscuity in DrugBank and ChEMBL are shown in Figure 2b–Figure 2d.

Figure 2. Drug promiscuity in DrugBank vs ChEMBL.

(a) For 518 qualifying drugs, the number of targets reported in DrugBank and the number of high-confidence activity annotations in ChEMBL are compared in a scatter plot. Each dot represents a drug. The diagonal (indicating perfect correlation) is drawn in red. In (b), (c), and (d), exemplary drugs are shown that had the same number of targets in DrugBank and ChEMBL (i.e., the same promiscuity rate), a higher promiscuity rate in DrugBank, and a higher rate in ChEMBL, respectively. For each drug in (b), the number of targets is given. For each drug in (c) and (d), the numbers of targets reported in DrugBank and ChEMBL are compared. For example, “10 | 6” indicates that the drug was annotated with 10 targets in DrugBank and with six in ChEMBL.

Differences in promiscuity rates were quantified, as reported in Figure 3a. Among the 486 drugs (~94%) with varying degrees of promiscuity in DrugBank and ChEMBL, 48 and 58 drugs differed by one and two targets, respectively. By contrast, the promiscuity rates of nearly half of the drugs (247; ~48%) varied by more than five targets. Moreover, for the 10 drugs shown in Figure 3b, the promiscuity rates differed by more than 30 targets, which reflected a particularly high degree of data inconsistency. All of these drugs were annotated with many more targets in DrugBank than targets derived from high-confidence activity records in ChEMBL. The extreme case was olanzapine the promiscuity rate of which differed by 47 targets between the two databases.

Figure 3. Promiscuity variation in DrugBank vs ChEMBL.

(a) Reported is the distribution of promiscuity differences (∆Activities) between DrugBank and ChEMBL for 518 drugs. (b) Shown are 10 drugs with the largest difference in promiscuity (∆Activities > 30). Target annotations are represented according to Figure 2c and Figure 2d.

In addition to comparing the number of target annotations, the activity profiles of drugs were further examined to determine the consistency of the annotations. As reported in Figure 4, 175 drugs (~34%) had non-overlapping sets of targets in these two databases, which was another surprising finding. The remaining 343 drugs had overlapping yet distinct target sets. However, the majority of these drugs shared only one or two targets, reflecting substantial discrepancies between target annotations.

Figure 4. Comparison of activity profiles in DrugBank and ChEMBL.

The activity profiles of 518 drugs in DrugBank and ChEMBL are compared. Reported is the number of drugs sharing increasing numbers of activities (#Common activities) in the two databases.

For the study of changes in drug promiscuity over time, accessing original activity records and their release dates was an essential requirement, as rationalized above. Such information is not available in DrugBank.

Drugs on a time course

Next, we organized the 518 drugs on the basis of activity record release dates. Drugs were assigned to the individual time intervals in which high-confidence activity data were first published. For example, if the first activity record of a given drug was detected in 2005, the drug was assigned to the 2005 interval and traced during all subsequent years. The cumulative number of drugs in different time intervals is reported in Figure 5a. By 2000, high-confidence activity data were publicly available for 78 drugs. From 2000 to 2001, activity data became available for 26 additional drugs. The number of drugs for which qualifying activity records were available in subsequent years ranged from 20 to 64, with an average of ~34 drugs per interval. The largest increase was detected for 2007/2008. The time period for which the activity records were assembled spanned a maximum of 24 years (for captopril, from 1981 to 2005), with an average of 3.3 years per drug. Exemplary drugs for which activity records were first reported before 2000 and after 2008 are shown in Figure 5b and Figure 5c, respectively.

Figure 5. Monitoring high-confidence drug activity data over time.

(a) Reported is the cumulative number of drugs for which high-confidence activity data became available in different years. In (b) and (c), six exemplary approved drugs are shown for which high-confidence activity data were first recorded before 2000 or after 2008, respectively. For each drug, its name, year of first data report, and therapeutic indication are provided.

Changes in drug promiscuity over time

For individual time intervals, the distribution of drug promiscuity rates was determined, as reported in Figure 6a. The box plots reveal an increase in drug promiscuity rates over time, with a maximal rate of six targets per drug in 2000 and 24 targets per drug in interval >2012. However, median promiscuity rates only slightly increased from one (until 2005) to two (beginning in 2006) targets per drug. The distribution of average promiscuity rates is shown in Figure 6b, which slightly but steadily increased over time from 1.5 to 3.2 targets per drug. The larger relative increase of average than median promiscuity rates indicated that the average values were influenced by small numbers of drugs with large numbers of targets, i.e., a small subset of highly promiscuous drugs, consistent with earlier observations¹¹. On the basis of median values, detectable increases in drug promiscuity over time were limited.

Figure 6. Monitoring drug promiscuity over time.

(a) Box plots capture the distribution of the number of targets per drug in different years. Each box plot reports the smallest value (bottom line), lower quartile (lower boundary of the box), median (thick horizontal line), upper quartile (upper boundary of the box), and the largest value (top line). (b) Reported are average numbers of targets per drug in different years.

Changes in promiscuity over time were also monitored for individual drugs. For each drug, the increase in the cumulative promiscuity rates from its first to its most recent activity records was determined (for the hypothetical example in Figure 1, the increase in promiscuity rates is 2). For the 518 drugs, increases are reported in Table 2. Surprisingly, for 282 drugs (~54%), no increase in promiscuity was detected on the basis of high-confidence activity records. This indicated that the majority of these drugs did not receive additional high-confidence activity annotations since their first records were released. Of 282 drugs, 203 drugs were only annotated with a single target. Exemplary drugs with constant promiscuity rates are shown in Figure 7. For the remaining 236 drugs, increasing numbers of targets were detected. However, in most cases, the increase in target numbers was limited, i.e., the promiscuity rates of 197 drugs increased by one to five targets (Table 2). There were only 14 drugs with an increase in promiscuity rates by 10 or more targets. Five drugs with largest increase in promiscuity rates are shown in Figure 8. For example, the promiscuity rate of imatinib increased from one in 2002 to 24 (>2012), with 11 new targets reported between 2008 and 2009. The drugs in Figure 8 belonged to the subset of highly promiscuous drugs that statistically influenced the calculation of average promiscuity rates, as discussed above.

Figure 7. Drugs with constant promiscuity over time.

Shown are 12 exemplary drugs having a constant promiscuity rate on the basis of high-confidence activity data. For each drug, the year of its first activity report and the number of targets it was active against are given. For example, brimonidine was first reported to be active against a single target in 1997.

Figure 8. Top five drugs with largest changes in promiscuity.

For the five drugs with largest changes in promiscuity over time, cumulative numbers of targets are reported for different years (top). For each drug, the overall difference in target annotations is given in parentheses. The structures of these drugs are shown at the bottom.

Drug promiscuity across different target families was also assessed. For the 236 drugs with increases in promiscuity rates over time, their targets were assigned to families and the number of target families was determined and followed over time. Table 3 reports the number of drugs with increasing target family annotations. For the majority of drugs, the number of target families increased by one or two. For top five drugs with largest changes in promiscuity (Figure 8), their target family profiles are provided in Table 4. The first activity records of all these five drugs belonged to only one target family including protein kinase family, GPCR subfamily, and transporter subfamilies. Compared to their most recent activity records, the number of target families increased by three to nine, spanning a wide range of related or unrelated target families. It indicated that these drugs might have been tested against a large panel of targets over time and that a number of activities have been confirmed at a high level of confidence. For 47 drugs, the number of target families remained constant.

Drug promiscuity on the basis of low-confidence data sets

Two compound sets with lower activity data confidence were also assembled from ChEMBL, as described above. The composition of these sets is summarized in Table 5. Low-confidence set 1 in which the types of activity measurements were not specified contained a total of 605,206 compounds active against 2144 targets, yielding more than 2,600,000 interactions. Low-confidence set 2 in which, in addition, the confidence level of activity was undefined consisted of a larger number of 936,924 compounds active against 3934 targets, yielding more than 6,000,000 interactions. All 518 drugs were mapped to these two low-confidence data sets. The cumulative distribution of these drugs over time is reported in Figure 9a. The number of drugs with low-confidence activity annotations in 2000 increased from 78 (high-confidence set) to 194 (low-confidence set 1) and 335 (low-confidence set 2). On average, ~26 and ~15 drugs became available during each year for low-confidence set 1 and 2, respectively.

Figure 9. Drugs in sets with varying activity confidence levels.

(a) Reported is the cumulative number of drugs in three data sets of varying confidence levels over time. (b) Shown is the distribution of average promiscuity rates for drugs in these three data sets. (c) For imatinib, the cumulative number of targets is reported for different years.

Figure 9b compares the distribution of average drug promiscuity rates for the three data sets over time. In contrast to the high-confidence data set in which drug promiscuity only slightly increased over the years, the average promiscuity rates of drugs in both low-confidence sets were higher and significantly increased. In low-confidence set 2, the average promiscuity rate was 6.3 targets per drug in 2000 and further increased to 28.2 targets (>2012). Thus, by reducing the stringency of selection criteria for activity records, high average promiscuity rates were obtained. The large increases in average promiscuity rates seen in Figure 9b ultimately resulted in 18 (low-confidence set 1) or nearly 30 (set 2) targets per drug are most likely artificial in nature. The comparison reveals how the choice of different activity data selection criteria, or the lack of well-defined criteria, might bias promiscuity analysis.

Imatinib represented a striking example for the presence of unreliable target annotations under non-stringent data selection criteria (Figure 9c). In both low-confidence sets 1 and especially 2 dramatic increases were observed between 2005 and 2008, ultimately leading to 406 and 689 targets for imatinib, respectively (hence exceeding the total number of targets in the human kinome). By contrast, on the basis of high-confidence activity data, the final (>2012) promiscuity rate of imatinib was 24.

In addition, the distributions of potency values were compared across different data sets, as reported in Figure 10. It should be noted that only K_i and IC₅₀ values were considered here, although all other types of potency annotations were included in low-confidence sets 1 and 2. In general, the distribution of the high-confidence set was comparable to the low-confidence set 1. The majority of negative logarithmic potency values ranged from ~4.5 (i.e., ~32 µM) to 7.0 (i.e., 100 nM). By contrast, the majority of potency values in low-confidence set 2 were confined to a narrow range.

Figure 10. Potency distribution.

The distribution of pK_i and/or pIC₅₀ values in three data sets with varying confidence levels is reported in box plots. Each box plot provides the lowest potency value within the 1.5 interquartile range of the lower quartile (bottom line), lower quartile (lower boundary of the box), median value (thick line), upper quartile (upper boundary of the box), and the highest value within the 1.5 interquartile range of the upper quartile (top line). Potency values falling outside these ranges are indicated by empty circles.

PAINS substructures

Compounds that are reactive or cause other non-specific effects in a variety of assays are typically false positives and have been termed pan assay interference compounds (PAINS)¹⁶. Baell and Holloway described a set of 26 substructures that are indicative of PAINS liability)¹⁶. This set of substructures was utilized as a filter to identify drugs that contain PAINS substructures in our three data sets with varying activity confidence levels. A total of 23 drugs (i.e., ~4.4%) were found to contain PAINS substructures. Figure 11 reports the average promiscuity rates of PAINS-positive drugs compared to all available drugs over time. It can be seen that drugs with potential PAINS liability in two low-confidence sets displayed much higher degrees of promiscuity than the global rates. For example, the latest average promiscuity rate (i.e., >2012) of drugs containing PAINS substructures in low-confidence set 2 increased from ~28.2 to ~67.0. By contrast, PAINS-positive drugs in the high-confidence set displayed a comparable degree of promiscuity. These findings suggest that PAINS-related effects might also be controlled by applying rigorous data confidence criteria.

Figure 11. Drugs containing PAINS substructures.

Shown is the distribution of average promiscuity rates over time for all drugs (solid lines) and for drugs that contain PAINS substructures (dashed lines) in three data sets with varying activity confidence levels, respectively.