A Prospective Virtual Screening Study: Enriching Hit Rates and

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A Prospective Virtual Screening Study: Enriching Hit Rates and Designing Focus Libraries to Find Inhibitors of PI3K# and PI3K# Kelly L. Damm-Ganamet, Scott D. Bembenek, Jennifer W Venable, Glenda G Castro, Lieve Mangelschots, Danielle C. G. Peeters, Heather M Mcallister, James Patrick Edwards, Daniel Disepio, and Taraneh Mirzadegan J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.5b01974 • Publication Date (Web): 04 Apr 2016 Downloaded from http://pubs.acs.org on April 5, 2016

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Journal of Medicinal Chemistry

A Prospective Virtual Screening Study: Enriching Hit Rates and Designing Focus Libraries to Find Inhibitors of PI3Kδ δ and PI3Kγγ

Kelly L. Damm-Ganametτ†, Scott D. Bembenekτ†∗, Jennifer W. Venableθ , Glenda G. Castroτ, Lieve Mangelschotsτ, Daniëlle C. G. Peetersτ, Heather M. Mcallisterτ, James P. Edwardsτ, Daniel Disepioτ and Taraneh Mirzadeganτ

τ

Discovery Sciences and θImmunology, Janssen Research & Development, San Diego, CA

92121, USA.

ACS Paragon Plus Environment


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ABSTRACT Here,

we

report

a

high-throughput

virtual

screening

(HTVS)

study

using

phosphoinositide-3-kinase (both PI3Kγ and PI3Kδ). Our initial HTVS results of the Janssen corporate database identified small focused libraries with hit rates at 50% inhibition showing a 50-fold increase over those from a HTS (high-throughput screen). Further, applying constraints based on ‘chemically intuitive’ hydrogen bonds and/or positional requirements resulted in a substantial improvement in the hit rates (versus no constraints) and reduced docking time. While we find that docking scoring functions are not capable of providing a reliable relative ranking of a set of compounds, a prioritization of groups of compounds (e.g. low, medium and high) does emerge, which allows for the chemistry efforts to be quickly focused on the most viable candidates. Thus, this illustrates that it is not always necessary to have a high correlation between a computational score and the experimental data to impact the drug discovery process.

KEYWORDS High-throughput screen, high-throughput virtual screen, virtual s, phosphoinositide-3 kinase, PI3K


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Introduction High-throughput virtual screening (HTVS) is often employed at the early stages of a drug discovery campaign to evaluate millions of compounds with the expectation of identifying a chemically novel lead series. The goal of HTVS is to accurately predict the intermolecular interactions between a small molecule ligand and protein and prioritize those compounds that may be potentially active against the target of interest. Today, many simplifications, such as neglecting protein flexibility, entropy, and solvation, are employed to reduce the computation time of screening large molecular databases. The main challenge then becomes one of balancing this increased speed and resulting loss of accuracy to insure good hit rates. While HTVS can reliably filter out molecules that have steric clashes with binding site residues (for the single protein conformation being utilized) or very different electrostatic properties, more subtle chemical differences or potential induced fit conformations may not be captured, resulting in both false negatives and false positives. HTVS is often used in conjunction with experimental high-throughput screening (HTS), where large chemical databases (thousands to millions of compounds) are screened in biological assays for activity. HTS is routinely utilized in the pharmaceutical industry as a method for hit identification but is not always accessible to smaller biotech companies or academic laboratories. Due to its high-throughput nature, this technique often suffers from a large number of false negatives and false positives. Moreover, we have found that many hits can be problematic compounds, and the time to triage hits to identify those worth pursuing can be significant if one’s HTS screening deck has not been thoroughly vetted. Further, often all corporate compounds are not included in the HTS screening deck. Finally, a high-throughput assay may not be technically feasible. On the other hand, HTVS allows one to interrogate much larger libraries both real and



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virtual. Therefore, a ‘screen by catalog’ (similar to ‘SAR by catalog’) of external libraries, or a screen of one’s full internal library (including compounds not in the screening deck, and/or those not available as physical samples) can serve to augment the data from a HTS screen. Various studies exist where the performance of HTVS and HTS are compared.1-11 These studies found that HTVS can: 1) identify different molecules than HTS, 2) enrich hit rates of HTS by creating focused libraries for experimental screening, and 3) be a valuable post-filter of HTS data. Interestingly, Babaoglu et al1 also found that most screening artifacts are a result of the compound’s physical behavior, not its reactivity. The fact that HTVS can identify alternate molecules than HTS suggests that compounds found by both methods should be given a higher priority when determining which hits to take forward. Indeed, HTVS can and should be used as a method to complement HTS. A review by Irwin and Shoichet provides an excellent overview on the recent advances in molecular docking.12 An internal HTS campaign to identify inhibitors of phosphoinositide-3-kinase gamma (PI3Kγ) provided the necessary experimental data to assess and compare the results of complementary HTVS and HTS studies. The learnings from this study were then leveraged in an additional HTVS to create a small focused library for screening against phosphoinositide-3kinase delta (PI3Kδ). Phosphoinositide-3-kinases (PI3Ks) are lipid kinases that catalyze the synthesis of the phosphatidylinositol second messenger, phosphotidylinositol (3,4,5)triphosphate (PIP3).13 Membrane-anchored PIP3 acts as a docking site for multiple signaling proteins, leading to the activation of downstream effectors such as AKT and BTK.14 To date, four Class I PI3K isoforms have been identified and maintain high sequence identity in the catalytic domain; Class IA includes the α, β, and δ isoforms, which are activated by receptor tyrosine kinases, and Class 1B includes the γ isoform, activated by G-protein coupled receptors


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(GPCRs).15 PI3Kγ has been shown to be involved in inflammation and immune responses and as such has emerged as a drug target for rheumatoid arthritis, lupus, and other immune mediated disorders.16 In recent years, mouse models have shown that the loss of activity of both PI3Kγ and PI3Kδ isoforms has greater effects on adaptive and innate immune function than the loss of either one alone. Presumably this is due to the immune-cell-specific expression and nonoverlapping roles of PI3Kγ and PI3Kδ.14 Consequently, a growing interest in the therapeutic benefit of having a dual inhibitor of both isoforms has occurred in recent years. Here, we report a study using PI3Kγ and PI3Kδ where we examined 1) high-throughput virtual screening (HTVS) versus high-throughput screening (HTS) hit rates, 2) the ability of constraints to further enrich HTVS, 3) the GlideScore role in HTVS hit rates, and 4) how these learnings could optimize the identification of small focused libraries. Initially focusing on PI3Kγ, we compared HTS and HTVS hit rates from both prospective and retrospective studies. Our results demonstrate that HTVS hit rates are often higher than those of HTS. We then examined the effects of including docking constraints such as hydrogen-bonding and positional requirements to enrich HTVS results. In general, the addition of docking constraints significantly reduced the amount of conformation search space and the time spent docking each individual ligand. Ligands with “inappropriate” chemical features are unscored, further increasing the efficiency of the HTVS. We find that the HTVS predictions are enriched by including such constraints illustrating the importance of utilizing a hybrid approach and integrating information on the known bound conformations of other ligands. We also examined the effect of the GlideScore cutoff on the hit rate and found that while docking scoring functions are not capable of providing a reliable relative ranking of a set of compounds, a prioritization of groups of compounds does emerge. Typically, as the GlideScore cut-off increases the rate of false positives



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decreases but true positives are also missed. The learnings were then applied to optimize a subsequent HTVS for PI3Kδ where ~61% of the prospectively identified compounds showed a pIC50 of ≥ 5. Given our overall findings, we discuss the benefits of using HTVS along with traditional HTS screens.

Results PI3Kγγ Prospective High-throughput Virtual Screen. Here, we conducted an HTVS to create a focused library for experimental testing with the goal of identifying a lead compound for the PI3Kγ campaign. In virtual screening, large databases of mostly inactive compounds are screened with the hopes of selecting the actives in the top fraction of the database. As such, a benchmark study was completed with the purpose of determining the most optimal Glide HTVS parameters for docking into PI3Kγ. We evaluated the ability of Glide, using varying hydrogenbond and positional constraints, to identify known PI3Kγ inhibitors in the top fractions of ranked data sets. The locations of the constraints are provided in the supporting information (S1) and were determined by recognizing which interactions are the most significant across various chemical series. The hydrogen bond constraints were placed to capture the hinge-binding region of PI3Kγ where an acceptor of the ligand would make interaction with the backbone amine of the hinge (V882) and a donor of the ligand would make interaction with the backbone carbonyl of the hinge (V882). Additional hydrogen bond constraints were utilized to capture donor and acceptor interactions with the carboxylic acid side chain and backbone amine of D964, respectively. The positional constraints required the ligand to occupy space near V882 or D964. In the benchmark screens, requiring 2 of 4 hydrogen-bond constraints (HBx2) performed slightly better than the others (data from the benchmarking study can be found in the supporting


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information (S2)). In addition to identifying a large percentage of the known actives, only a small portion of the database actually satisfied the constraints and was docked. This can significantly speed up the HTVS calculation, which is very important in a screen with millions of compounds. Requiring 3 of 4 hydrogen-bond constraints (HBx3) also performed quite well; however, it is important to note that as more constraints are applied, there is always the possibility that it may not lead to a set of diverse chemotypes. As such, we determined that HBx2 was the most appropriate constraint for the subsequent docking screens. For this protein system, hydrogen-bond constraints showed superior performance over positional constraints. To ensure that reasonable docking poses were also being found, we examined the predicted binding pose of the PI3Kγ inhibitor N-(5-(4-chloro-3-(2-hydroxy-ethylsulfamoyl)phenylthiazole-2-yl)-acetamide “1 (PIK-93)”17. Unfortunately, at the time this work was performed, this was the only compound in our benchmarking dataset with a published binding mode. Glide was able to reproduce the binding mode of the ligand in complex with PI3Kγ (PDB ID: 2CHZ17), as shown in Figure 1. The RMSD between the predicted pose and the crystal structure is 1.13 Å. Further inspection showed that the electron density on the ‘tail” portion of the molecule is missing, which is the area with the least overlap between the predicted pose and crystal structure. Removing the atoms from this portion (hydroxyl and two carbon chain) results in an RMSD of 0.67 Å. In HTVS, because a large number of compounds are being screened, a cutoff score value is employed to determine if a compound is further pursued for biological testing rather than visual inspection of all poses. Unfortunately, the rank of a compound is not always based on a correct pose prediction. The Community Structure Activity Resource (CSAR) benchmarking exercise from 2011-2012 found that the correct pose is not a necessity for ranking correctly, but there is a better chance to be scored correctly if the pose is correct as well.18



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Figure 1 Using the optimal P-HBx2 (prospective study, 2 of 4 hydrogen bonds required) constraint found from the benchmarking study, a Glide HTVS was employed to build a “focused” library of potential PI3Kγ inhibitors from the Janssen corporate library to supplement an ongoing HTS effort. A GlideScore cutoff of ≤ -8 was utilized resulting in a hit list of 7192 unique molecules. A cutoff value of ≤ -8 was chosen based on prior docking experience with the PI3Kγ project and the resulting scores from the benchmarking study. Additional filters were employed (see Methods section for details) resulting in a list of 1721 unique molecules. To further enhance the “focused” library, any molecule with a GlideScore of < 0 from the initial run (200,731 molecules) was re-screened but requiring P-HBx3 (prospective study, 3 of 4 hydrogen bonds) to be satisfied. As the applied constraint in this run was very stringent (only 2793 unique molecules were actually docked), a GlideScore cutoff of ≤ -7 was utilized resulting in a hit list of 636. The previous hit list of 1721 molecules was combined with the 636 molecules and after removing the duplicates (271 molecules), a final list of 1623 predicted molecules was presented to the HTS team for biological testing. Of the 1623 predicted molecules, 1478 were actually tested in the subsequent HTS (“HTVS compounds”) due to availability. In addition to the 1478 compounds identified by the HTVS, 58,371 compounds from an external Kinase focused library (“HTS compounds”) were assayed. It was confirmed that these compounds were not present in the HTVS list. Of the 58,371 compounds, 1,901 showed ≥ 50% inhibition in the primary assay at a concentration of 10 µM and were taken forward to the confirmation assay. Confirmation data is more reliable than the primary data as each data point is an average of three measurements from the confirmation assay, while each value is only determined once for the primary data.


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Table 1 shows the hit rates of the HTVS and HTS compounds across varying levels of percent inhibition determined from the confirmation biological assay (at 10 µM). At each level, the hit rates are significantly better for the compounds predicted from the HTVS. For example, at 50% inhibition, the HTVS hit rate was 10.3% compared to only 0.2% from HTS, a 50-fold increase. While a large pharmaceutical company may be able to assay millions of compounds, this typically is not a viable option for smaller Biotech or academic labs. Our data provides support that virtual screening can be utilized to create focused libraries with very good hit rates. Table 1 In order to ensure that the HTVS identified diverse chemistry and not just similar chemotypes, the molecular similarity between the hit list compounds at ≥50% inhibition was compared by calculating the Tanimoto coefficient using Pipeline Pilot19 (ECFP_4 fingerprints). Table 2 provides a breakdown of the percentage of compounds within varying Tanimoto cut-off values. As an example, of the Tanimoto coefficients calculated for the 152 compounds with ≥50% inhibition (i.e. 152x152 matrix), 95.5% were less than 0.25. This data demonstrates that the compounds were indeed very dissimilar and a wide range of chemical chemotypes were identified by the virtual screen. Table 2 Select HTVS compounds were chosen to take forward for dose response data based on their activity in the confirmation assay (≥ 50% inhibition cut-off) and visual inspection by the CADD and chemistry team resulting in a final list of 66 molecules. As such, this was not a complete follow-up of compounds at the 50% inhibition cut-off but rather a very focused and selective effort where compounds were cherry picked that the project team was interested in moving forward. Due to tight screening criteria, one often sees this happen at this stage of an



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industrial drug discovery project. Of the 66 identified hits, 10 of the compounds tested showed activity at a pIC50 of ≥ 5. Table 3 provides examples of identified compounds from the PI3Kγ HTVS along with their respective dose response data (pIC50). Here, we have not necessarily shown all of our best examples due to project constraints; however, the compounds provided are some of our most active. To demonstrate the additional diversity of the identified set, the molecular similarity between the top ten compounds with a pIC50 of ≥ 5 was also compared by calculating the Tanimoto coefficient and examining various similarity cutoffs as shown in Table 2. As an example, of the Tanimoto coefficients calculated for the 10 compounds with pIC50 of ≥ 5 (i.e. 10x10 matrix), 82.2% were less than 0.25. Table 3

Running multiple virtual screens of large corporate databases can be more time consuming than applying ligand based approaches as such the question is raised if the identified compounds could have been found using simpler approaches such as 2D similarity or substructure searching. To answer this, the similarity was calculated between the top ten compounds with a pIC50 ≥ 5 and a database of 123 PI3Kγ inhibitors from the literature. The database used is a subset of the Kinase Knowledgebase (KKB) from Eidogen-Sertanty20, a SAR database of kinase inhibitors curated from scientific literature and patents. As of the 2015 Q4 release, there are 331,141 unique compounds with assay data. The KKB was filtered for PI3Kγ compounds with pIC50 or pKi values ≥ 5 resulting in 12,442 unique compounds. These compounds were then clustered using in-house methodology21 based on the concept of maximum common fingerprints at a level of 0.3 and a cluster head was randomly chosen, resulting in a final list of 123 very diverse, active structures. Figure 2 shows a histogram of the Tanimoto


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coefficients for the top ten compounds compared to the 123 known PI3Kγ inhibitors. The majority of the Tanimoto coefficients are ≤ 0.30, hence, it is very unlikely that these hits would have been identified using a simple, 2D similarity method as they are below the typical ‘rule of thumb’ Tanimoto coefficient cut-off of ≥0.5. Figure 2 The HTVS hit list presented above is a combination of predicted compounds from two virtual screens, one requiring 2 of 4 hydrogen-bond constraints to be fulfilled (P-HBx2) and the other requiring 3 of the 4 hydrogen-bond constraints (P-HBx3). This raises the question of which of the two screens resulted in the most confirmed hits. A comparison of the compound hit rates at varying percent inhibition levels for both P-HBx2 and P-HBx3 is shown in Table 1. Across the varying percent inhibition levels, the difference in their performance was 1-2% demonstrating that both sets of constraints performed similarly and predicted a comparable percentage of active compounds. However because P-HBx2 is less restrictive, a greater number of active compounds were identified than in P-HBx3, which may lead to increased chemical diversity in this compound set. It is important to have multiple chemotypes for a particular project at this stage allowing for triaging of hits earlier based on properties like superior ADME/developability, selectivity, among others. An interesting observation is that the intersection of the predicted molecules from the two virtual screens (HBx2∩HBx3, the compounds in common between the screens) has a slightly increased hit rate; for example, at 50% inhibition it jumps up to 15.5% compared to 10.2% and 12.4% for HBx2 and HBx3, respectively. Additionally, the rate of 15.5% is a 75-fold increase compared to the HTS results at 0.2%. This data suggests that conducting multiple HTVS with varying constraints can lead to further ‘enriched’ focused libraries. All molecules identified in the multiple screens below a



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certain energy threshold should be compared and the intersection included in a prioritized list for experimental testing. The use of constraints and intersections of multiple screens can be a method for overcoming the limitations of scoring functions and help increase hit rates. PI3Kγγ Retrospective High-throughput Virtual Screen. To complement our prospective HTVS study, we also conducted a retrospective Glide HTVS of the 58,371 ‘HTS compounds’ from the external Kinase library previously tested and used for a comparison in our prospective study. As a baseline prediction, this virtual screen did not employ positional or hydrogen-bond constraints. Of the 58,371 HTS compounds, only 1,836 were predicted to fit the pocket well enough to result in a negative GlideScore in our virtual screen. In a prospective study, this would be the focused set taken forward for experimental testing, a large reduction of the original 58,371 compounds assayed. Based on the HTS confirmation data, there are 134 compounds with activity above the 50% inhibition level at a concentration of 10 µM. Using this level as a cutoff for activity, a docking enrichment factor (EF) at 2% of the dataset was calculated to quantify the HTVS’ performance. The EF is calculated as EF% = {Ligandsselected/N%} / {Ligandstotal/Ntotal}22. It is an assessment of the ability of the HTVS to find true positives among the entire dataset as compared to random. At 2% of the dataset (EF2%) when no constraints were employed, there is a 6.6-fold enrichment over a random selection. Three hit lists were generated using three different GlideScore filters (GlideScore cutoffs of ≤ 0, ≤ -7 and ≤ -8) in order to assess the dependence of Glide’s performance on the score cutoff utilized. The hit rates from the three hit lists across varying percent inhibition levels are provided in Table 4. When a GlideScore cutoff of ≤ 0 was employed, 131 of the 134 actives compounds were identified; hence, there were only 3 false negatives. However at this cut-off,


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there are a large number of false positives identified (1705) resulting in a hit rate of 7.1%. As the GlideScore becomes more stringent and only the top percentage of the hit list is examined, an improved hit rate is observed. For example, when a GlideScore cutoff of ≤ -8 is applied, the hit rate jumps from 7.1% to 54.5% at 50% inhibition (~7.5x improvement); over half of the compounds show activity at this level. The downside though is that as the rate of false positives decreases, true positives were also not being captured. Table 4 Figure 3A shows the GlideScore plotted against percent inhibition from the confirmation assay. Three distinct GlideScore thresholds are apparent: ≥ -3, -3 ≤ x ≥ -7 and ≤ -7. At the threshold of GlideScore ≥ -3, there were no compounds with 50% inhibition or greater. It appears that for this particular system, when no constraints were utilized, the scoring function is able to discriminate compounds without activity at this cutoff level. This is an important observation; although it is very difficult to rank the most active compounds, one can have confidence that at this lower bound score cut-off, there is a hard wall for those without any activity. For a GlideScore of -3 ≤ x ≥ -7, there are a large number of false positives, resulting in a much lower hit rate. Within this range, it is very difficult to determine which compounds are truly active. Finally, for a GlideScore of ≤ -7 the number of false positives is greatly reduced, and it would be very efficient to test this set of compounds. It is not surprising that scoring functions cannot identify all of the active molecules as the top scorers. However, as we see, they do provide a tiering where groups of compounds can be identified that are worth pursuing and groups that can be discarded. Figure 3



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The results from our prospective studies showed that the use of constraints could improve virtual screening hit rates. As such, we conducted two additional Glide HTVS using 1) R-HBx2 (retrospective study, 2 of 4 hydrogen bonds) and 2) R-HBx3 (retrospective study, 3 of 4 hydrogen bonds) to assess if these finding would hold true in our retrospective study. We do again see this trend, as shown in Table 5. Comparing the results of the R-HBx2 and R-HBx3 runs (GlideScore ≤ 0) to the no constraint HTVS, we see ~2.5x improvement for R-HBx2 and ~7.5-8x for R-HBx3 across the varying inhibition levels. Figures 3B and C provide the GlideScore plotted again percent inhibition at 10 µM from the confirmation assay for the RHBx2 (248 compounds) and R-HBx3 (24 compounds) runs. Similar to the no constraint HTVS, we see the three GlideScore thresholds (≥ -3, -3 ≤ x ≥ -7 and ≤ -7). However, for R-HBx3 this threshold decreases to a GlideScore of ≥ -5.5 from GlideScore of ≥ -3 and the number of false positives overall is reduced. Again, there is the caveat that the number of active compounds identified is decreased and the compounds may be similar due to the criteria selecting out similar chemical functionality if the constraints are too stringent. Yet, the hit rates are far better than what can be achieved by HTS and a small number of compounds is enough to kick start a project and allow the team to move forward with initial leads. Furthermore, this analysis was completed on a set of ~58K compounds. One could imagine that these trends would scale for a larger dataset of millions of compounds resulting in the identification of a much larger set. Table 5

Finding a Small Focused Set for PI3Kδ δ. Our initial studies have shown that, in general, the use of virtual screening can lead to small focused libraries with hit rates that are substantially better than those from a HTS on an entire corporate library containing millions of compounds.


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Further, we found that utilizing a hybrid method by employing constraints which capture information on the known bound conformations of other ligands can result in a substantial improvement in the hit rates over the corresponding no-constraint virtual docking and shorten the overall docking time. Building off of these learnings, we sought to identify a focused set of 5,000 compounds with a high potential for binding to PI3Kδ (another one of the four isoforms in the PI3K family of kinases). Crystallography information has shown23 that certain PI3Kδ inhibitors with a “propellershaped” motif can induce a pocket in the P-loop region. Ligands interacting in this “specificity pocket” tend to have excellent overall kinome selectivity while showing a varying degree of activity over the PI3K isoforms.24 Indeed, one currently marketed drug, Idelalisib, and another late stage inhibitor, Duvelisib, leverage a “specificity piece” in their design. Idelalisib was approved in 2014 in the United States and European Union for the treatment of relapsed/refractory chronic lymphocytic leukemia (CLL, in combination with Rituximab), relapsed follicular lymphoma, and relapsed small lymphocytic lymphoma (as monotherapy). In addition, it was approved in the European Union as first-line therapy for certain poor-prognosis CLL and in patients not suitable for chemoimmunotherapy. Further, Idelalisib has been reported to be highly potent and selective for PI3Kδ.25 The other compound of interest, Duvelisib, is currently in Phase III trials for chronic lymphocytic leukemia and in Phase II trials for indolent non-Hodgkin’s lymphoma. Duvelisib has been reported to be selective for PI3Kδ and PI3Kγ.14 Our goal then was to identify good PI3Kδ inhibitors containing a novel specificity piece. In our initial studies, we ran the virtual screening in HTVS mode since it provides a significant time savings over the higher precision modes available in Glide (SP and XP). Here, we apply a variation on this approach as our initial efforts found that the use of constraints not



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only facilitated improved docking poses but also resulted in higher throughput. Moreover, we wanted to leverage the known structural information about ligands binding to PI3Kδ, in particular around the hinge and specificity pocket regions. Therefore, we applied one hydrogen bond constraint (HBx1) for where the acceptor of the ligand would make an interaction with the donor of the hinge (V828), and a positional constraint (POSx1) requiring a portion of the ligand to occupy the specificity pocket. Given the significant number of good kinase inhibitors that make a single-point interaction with the hinge through an acceptor atom, our choice of hydrogen bond constraint seems justifiable. Moreover, finding inhibitors that were both active and selective was important to us, which motivated the specificity pocket constraint. Overall, we choose only these two constraints as to ensure enough freedom for finding diverse chemotypes. The crystal structure used for docking was that of the ICOS compound 2-[(6-amino-9Hpurin-9-yl)methyl]-5-methyl-3-(2-methylphenyl)quinazolin-4(3H)-one “2 (IC87114)” in PI3Kδ (PDB ID = 2X38)23. We note that the protein species used for this structure was mouse. The overall identity between human and mouse for PI3Kδ is 94.5% for the full length protein and 98.3% for the kinase domain alone; further, there are no residue differences in the immediate active site region proximal to the ligand (when using a cutoff of 9Å). As such, we did not anticipate significant structural variations at the time and later crystal structures have confirmed our initial thought. Our motivation for using this complex was that the ligand is of a similar chemotype to Idelalisib. Figure 4 shows the crystal structure 2X3823 with its ligand in complex with PI3Kδ. Here, the specificity piece is circled in red, and the hydrogen bond interaction used as the docking constraint is annotated. The main residues in the specificity pocket that interact with this specificity piece are I777 (of the β3-Strand), P758, W760, and M752 (of the P-Loop). Figure 4


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We first docked our Janssen corporate collection library in HTVS mode using the aforementioned constraints. This time we kept only those (unique) compounds that had a GlideScore cutoff of < -6.5. We then generated three conformations (at the most) for each compound, which became the starting conformations for another round of docking. In this subsequent docking we used the same constraints as before, but this time we implemented the higher precision SP mode. It is well known that the initial starting conformation can have an effect on the resulting docked pose, and generating multiple starting conformations is a meaningful way to offset this chaotic behavior. From this final docking run, we kept only those compounds with a GlideScore cutoff of < -7. Final filtering and clustering reduced this to the required set of 5000 compounds, with 4112 actually available for testing. The compounds were tested at 1 µM, 1.25 µM, and 10 µM. In Table 6 we see the confirmation hit rates from our virtual screening method for ≥ 40% inhibition at a concentration of 10 µM. The 311 compounds at 40% inhibition were whittled down further through clustering and chemists’ input, which resulted in 132 compounds chosen for testing in dose response. Table 7 shows the hit rate for pIC50 values of 5, 6, and 7. Here we see ~61% of the compounds tested showed activity at a pIC50 of ≥ 5, while over 5% of the compounds showed activity at the more stringent pIC50 of ≥ 6. These results are encouraging and once again, are a testament to the value of utilizing known information about your system in the form of constraints in HTVS. To be sure, the positional constraint most likely eliminated several potential hits since it enforces a 3D geometry often not seen in typical kinase inhibitors, thus making these results that much more impressive. Table 6, Table 7



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Examples of identified compounds from the PI3Kδ virtual screen are shown in Table 3, along with their respective dose response data (pIC50). In order to better quantify the diversity of the compounds found from the PI3Kδ virtual screen, we calculated the diversity matrix (using ECFP_4) for the 217 compounds that had ≥ 50% inhibition at a concentration of 10 µM. Using this information we were then able to determine the percentage of compounds within various similarity cutoffs. In Table 2 we see that the majority of compounds quickly converge to falling within a similarity of ≤ 0.35. In other words, the majority of these compounds show excellent diversity with respect to each other. Further, we did this for the 80 compounds at a pIC50 of ≥ 5. Once again, we see the convergence is quick, albeit to the slightly higher similarity cutoff of 0.50. Nonetheless, it is clear that the overall diversity of this smaller set is still very good.

Discussion and Conclusion At the onset of a drug discovery project, it is common practice to perform a highthroughput screen (HTS) to identify leads. From a computational chemistry perspective, highthroughput virtual screening (HTVS) is a fundamental tool often used to supplement and/or complement a HTS effort. Here, we demonstrate that high-throughput virtual screens can be utilized to create focused libraries with appropriate features. As previously shown for PI3Kγ, at 50% inhibition at a concentration of 10 µM, there was an enrichment of 50-fold increase in the HTVS hit rate over HTS. For both PI3Kγ and PI3Kδ prospective screens, we demonstrated that the resulting hits were a very diverse set of chemotypes shown by a low Tanimoto coefficient within the enriched hit list. Additionally, virtual screens can be very efficient; over 1.5 million compounds were screened in the PI3Kγ HTVS and after various filtering steps, only 1,478 molecules were deemed the appropriate size and chemical functionality to test experimentally. A


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further advantage is that HTVS can be utilized to complement internal screens with external databases that have commercially available molecules (‘screen by catalog’). This may allow companies to cover additional chemical space not fully represented in their corporate libraries, or to focus on libraries created for a specific target class such as kinases. Using HTVS to create ‘focused’ libraries results in a reduction of the number of compounds assayed experimentally, which will result in a dramatic cost and time savings thus allowing HTS to become a viable option for drug discovery researchers in both academia or industry. An additional goal of this work was to more closely examine our approach to highthroughput virtual screens, and determine what strategies could be utilized to overcome the limitations of scoring functions. There will always be a trade-off between speed and accuracy when trying to screen millions of compounds in a reasonable time frame. Therefore, one will always have to balance the inclusion of more sophisticated computational approaches with the actual reduction to practice. In this study, we show that including known information about a target (i.e. which residues are most important in binding, which area of the binding site should be occupied, etc.) in the form of hydrogen bond and positional constraints can significantly improve hit rates while speeding up overall time of the screen. It is well known that human intervention and the use of ‘chemical intuition’ in choosing compounds to move forward from screens can greatly improve hit rates. Unfortunately, when millions of compounds are being screened, it is not possible to manually examine each compound. However, the use of constraints is a way to include chemical intuition in filtering out compounds during the virtual screening step. The constraints that were utilized in this study were chosen based on our extensive work with PI3Kγ and PI3Kδ and by recognizing the most important interactions across various chemical series. In this way, we were able to add a layer of chemical intuition to the virtual screen. One must be



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cognizant to not over use known binding modes as this can limit the diversity of the molecules found. We again demonstrate through Tanimoto coefficients that the use of constraints did not result in a loss of diversity within the identified compounds as a variety of chemotypes were observed. Additionally, enrichment can be found by applying appropriate docking score cut-offs. Here, we show that the discriminatory limit for Glide HTVS scoring function when no constraints were applied was at a GlideScore of ≥ -3 for this protein system. Above this limit, there are no active compounds. For a GlideScore of ≤ -3, the scoring function did not provide adequate discrimination between active and inactive molecules. However, an upper threshold does become apparent at ≤ -7 where the number of false positives is greatly reduced. Docking scoring functions are not capable of providing a reliable relative ranking of a set of compounds, but we do see a tiering which allows for prioritization of groups of compounds (e.g. low, medium and high). In this way, we are able quickly focus the chemistry efforts on the most viable candidates. Thus, this illustrates that at the early onset of a discovery project (hit/lead finding) impact of HTVS can be very significant and as the project advances to a lead optimization stage other methodologies with more accurate scoring functions like FEP is more appropriate.

Materials and Methods Protein and Ligand Databases Preparation. A proprietary PI3Kγ co-crystal structure was chosen as a representative PI3Kγ conformation for Glide26, 27 HTVS docking. The protein structure was prepared and hydrogens added using the Protein Preparation Wizard available


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through Maestro from the Schrödinger Suite of Programs (Version 07) and the final result was visually examined. Default parameters were used for generating the docking grid. Two databases were created to evaluate the performance of the Glide HTVS when various constraints are applied and determine optimal parameters for subsequent PI3Kγ virtual screens. The first dataset contained 100 compounds consisting of 25 diverse known PI3Kγ inhibitors taken from the PDB, literature, and in-house compounds, 25 diverse compounds experimentally verified in-house to be inactive against PI3Kγ, 25 diverse known kinase inhibitors taken from the DUD (Databases of Useful Decoys) library28 and 25 diverse general drug-like decoys also from the DUD library. The second database contained the same 25 known PI3Kγ inhibitors and 1000 general drug-like ligand decoys provided by Schrödinger26. Default LigPrep parameters were used to generate tautomers, ionizations states (using Epik) and stereoisomers for both databases. A virtual database was previously created of a portion of the Janssen corporate library using default LigPrep parameters. The dataset used contained 1,510,503 unique compounds with various tautomeric and ionization states. A final virtual dataset was created of an external Kinase focused library comprised of 58,371 diverse molecules. Again, default LigPrep parameters were used to generate tautomers, ionizations states (using Epik) and stereoisomers. For the PI3Kδ virtual screen, we used the literature co-crystal structure of the ICOS compound IC87114 (PDB ID = 2X38). Once again, the protein structure was prepared and hydrogens added using the Protein Preparation Wizard. Thereafter, the ligand and the sidechains of the proximal residues T750, M752, W760, K779, D832, T833, N836, M900, I910, and D911, were minimized using MacroModel with the default parameters. This resulting protein structure was then used for generating the docking grid with the default parameter. Finally, the Glide



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database for the PI3Kδ virtual screen was generated using default parameters in LigPrep (in particular Epik was used for the ionization states rather than Ionizer). Glide HTVS Validation. Glide26, 27 (Version 07) was used to dock into the catalytic site of PI3Kγ using HTVS mode. Default parameters were used for molecular docking with the exception of applying contraints in some docking runs (docking was done with and without constraints). To determine the optimal parameters for the PI3Kγ HTVS, multiple validation docking runs were conducted. The validation studies included varying multiple constraints such as 1) no constraints, 2) requiring any 1, 2 or 3 of 4 hydrogen-bonds (h-bond) to be satisfied, 3) requiring any 1, 2 or 3 of 4 positional constraints to be satisfied, and 4) a combination of h-bond and positional constraints (any 1 h-bond/1 positional and any 2 h-bond/2 positional), while keeping all other parameters default. The hydrogen bond and positional constraints were placed to complement the hinge region Val 882 backbone amine and carbonyl and Asp 964 backbone amine and carboxylic acid side chain; details are provided in the supporting information (S1). The performance of the validation studies was evaluated by determining which set of constraints resulted in the discrimination of known active molecules from the decoy compounds. This was quantified by determining the number of known active molecules predicted in the top fractions of the scored molecules. The data is presented as 1) the “# Active” (total number of active molecules identified), 2) the “# Ranked” (total number of compounds ranked), and 3) the number of active molecules ranked in the top 2, 5, 10, 20, and 50% of the database (results are provided in the supporting information (S2)). All duplicate molecules were removed from the analysis. For example, if a ligand has multiple tautomers, the highest rank tautomer was retained for the analysis and the other(s) discarded.


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PI3Kγγ HTVS Screens. A virtual screen of a portion of the Janssen corporate library (~1.5 million compounds) was conducted using the optimal constraint determined in the validation studies (requiring 2 of 4 h-bonds to be satisfied; P-HBx2), again using default parameters and HTVS mode (prospective study). Analysis of the HTVS results was performed using Pipeline Pilot. All scored compounds were filtered using the following criteria: GlideScore of < -8, availability of the molecule for testing, compounds previously tested in a PI3Kγ assay, compounds known to be “frequent hitters” (i.e. test positively in multiple kinase assays), HTS filter (molecular weight < 150 and reactive functionalities) and molecules already to be screened in experimental HTS. This resulted in 1,721 unique molecules. Any compound with a GlideScore of < 0 (200,731 molecules) was then re-docked requiring a more stringent 3 of 4 hbonds to be satisfied (P-HBx3); all other parameters remained the same. Pipeline pilot was again used to filter the dataset; however, because the applied constraints were more stringent in this run, a GlideScore of < -7 was used as the cutoff. This resulted in 636 unique compounds, which had 271 in common with the previous 1,721. The lists were then combined (duplicates removed), totaling 2,086 molecules. The 2,086 molecules were then clustered by chemical similarity using an in-house proprietary technology, resulting in 1,623 clusters. For each of the 1,623 clusters, the molecule with the best GlideScore was chosen as the representative and included in a list for subsequent experimental testing. Of the 1,623 molecules, 1,478 were actually available for experimental verification. A second virtual screen of an external library (58,371 unique compounds) was conducted against PI3Kγ using Glide with default parameters (retrospective study). Three screens were completed where no constraints were applied and also employing the optimal constraints



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determined in the validation studies (requiring 2 of 4 and 3 of 4 h-bonds to be satisfied (R-HBx2, R-HBx3 respectively). Hit rates were calculated to quantify the successfulness of the HTVS. PI3Kδ δ HTVS Screens. A virtual screen of a portion of the Janssen corporate library (now ~2 million compounds) was conducted. This was first done in HTVS mode using one hydrogen bond constraint (HBx1) for where the acceptor of the ligand would make an interaction with the donor of the hinge (V828) and a positional constraint (POSx1) requiring a portion of the ligand to occupy the specificity pocket. A GlideScore cutoff of < -6.5 was imposed, resulting in 99,488 unique compounds that were then passed to ConfGen, where three conformers (at the most) per compound were generated for use in the follow-up SP docking run. From the SP docking run, we kept only those compounds with a GlideScore cutoff of < -7. Compounds were further whittled down using a Lipinski-like filter (where we change the molecular weight cutoff to ≤ 550, and the LogP cutoff ≤ 5.5), and on availability, which resulted in 37,781 unique compounds. Since our ultimate goal was to get the compound total down to no more than 5,000, we then clustered these compounds using the in-house proprietary clustering technology. This yielded 5,727 clusters from each of which the best GlideScore compound only was kept. Visual inspection of the resulting compounds, and an emphasis on testing those compounds already in soluble format gave the final number of 5,000; reduction to practice resulted in 4,112 compounds actually tested. From this set, 311 compounds had 40% inhibition at a concentration of 10 µM. These were reduced to 132 compounds to be tested in dose response through clustering and input from chemists. For this particular target, the total time for the HTVS run was ~10 days when using 30 CPU cores. PI3Kγγ High-Throughput HTRF Assay. A total of 58,371 compounds from in-house Janssen libraries were run in an antagonist screen in HTRF format (TR-FRET) at 10 µM. In the


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HTRF assay, PI3K enzyme is added to an assay mixture containing ATP, MgCl2 and phosphatidylinositol 4,5-bisphosphate (PIP2) (PI3Kγ has an apparent Km for ATP of 200 µM and 15 µM for PIP2). The PIP3 product is detected by displacement of biotin-PIP3 from an energy transfer complex consisting of Europium labelled anti-GST monoclonal antibody, a GSTtagged pleckstrin homology (PH) domain (GRP1), biotinylated PIP3 and StreptavidinAllophycocyanin (APC). Excitation of Europium in the complex results in an energy transfer to the APC and a fluorescent emission at 665 nm. The non-biotin labeled PIP3 product formed by PI3Kγ (h) activity competes with biotin-PIP3 for the PH domain in the complex resulting in a loss of energy transfer and thus a decrease in signal. 1,901 compounds exhibiting >2 standard deviation from the mean were identified as hits in the primary screen and run in triplicate to confirm activity. The 1,478 compounds identified by the HTVS were also run concurrently in triplicate. Hence, a total of 3,379 compounds were taken forward to confirm antagonist activity. In the scintillation assay, PI3K enzyme is added to an assay mixture containing cold and hot ATP, and its precoated substrate: ptdSER/ptdINOS. The kinase assay is performed in a 96 well microfluor plate. After washing, scintillation fluid is added and the plate is counted. Each data point is an average of 4-5 measurements. PI3Kδ δ LanthaScreen® Eu Kinase Binding Assay. LanthaScreen® Eu Kinase Binding Assay for PI3Kδ was performed in 384-well white proxiplates. PI3Kδ, Eu-anti GST-Antibody and kinase tracer 314 were purchased form Invitrogen. PI3Kδ, Eu-anti GST-Antibody and kinase tracer 314 were prepared in 1x kinase buffer from Invitrogen, which consists of 50 mM HEPES pH 7.5, 10 mM MgCl2, 1 mM EGTA and 0.01% Brij-35. The kinase binding reaction was started by addition of 5 µl PI3Kδ (0.5 nM final concentration) / Eu-anti-GST-Antibody (2 nM final concentration) mix to the pre-spotted compound plates (Labcyte Echo). For control, only 5



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µl Eu-anti-GST-Antibody (2 nM final concentration) was added. Immediately 5 µl kinase tracer 314 (10 nM final concentration) was added to start the displacement of the compounds. After 60 minutes incubation at room temperature fluorescence was measured with excitation of 320 nm and dual emission of 615 nM ( Eu signal ) and 665 nm ( FRET signal ) on the Envision reader (Perkin Elmer). Compound Purity Assessment. Compounds 1-5, and 14 were run on a Waters 2795 LCMS with a 2996 PDA and ZQ MS in ES+ mode. Chromatography was performed on a Merck Chromolith Flash RP-18 endcapped column, 25x4.6mm. A ternary gradient was used for compound elution. Mobile phase A consisted of 25mM ammonium acetate 90% water:10% acetonitrile. Mobile phase B was acetonitrile and mobile phase C was methanol. The gradient ramped from 96%A:2%B:2%B to 2%A:49%B:49%C over 0.9 minutes and then to 2%A:96%B:2%C over 0.55 minutes, holding at 2%%A:96%B:2%C for 0.05 minutes. Flow rate was 3 mL/min. Flow rate into the MS, controlled by flow splitter, was approximately 100 µL/min. Column was maintained at 30°C. A wavelength range of 200 – 400nm was used for PDA detection. Compounds 6, 8, 9, 11, 12, 13, 15, and 16 were run on a Waters Acquity UPLC with PDA and SQD in ES+/- mode using BEH C18 1.7µm column, 50 x 2.1mm. Mobile phase A was methanol. Mobile phase B consisted of 10mM ammonium acetate in 90% water:10% acetonitrile. The gradient ramped from 5% to 95% methanol over 1.3 minutes, holding at 95% methanol for 0.2 minutes before ramping back to 5% methanol over 0.2 minutes and holding for 0.3 minutes. The flow rate was 0.7 mL/min and the column was maintained at 70°C. A wavelength range of 210 – 400nm was used for PDA detection.


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Compound 10 was run on the same instrumentation using the same column, mobile phases and PDA settings. The gradient ramped from 5% to 95% methanol over 1.3 minutes, then ramping back to 5% methanol over 0.7 minutes. The flow rate was 0.8 mL/min and the column was maintained at 55°C. Compound 7 was run on a Waters 2795 LCMS equipped with 2996 PDA, Antek 8060 CLND, and ZQ in ES+/- mode using an XTerra MS, C18 3.5µm column, 100 x 4.6mm. Mobile phase A consisted of 0.1% formic acid in 95% water:5% methanol. Mobile phase B was methanol. The gradient ramped from 0 – 95% methanol over 12 minutes and then ramped back to 0% methanol over 1 minute. The flow rate was 1.5 mL/min. Using a flow splitter, flow rate into the CLND was approximately 150 µL/min; flow rate into the MS, was approximately 100 µL/min. Column was maintained at 40°C. A wavelength range of 200 – 400nm was used for PDA detection. All mobile phases were spectroscopic grade. Purities were calculated using the PDA signals, excluding DMSO absorbance. All purities were found to be ≥ 95%.

Acknowledgements. We thank Brett Tounge for his help in creating the Glide version of the Janssen corporate database that was used for the PI3Kγ virtual screen. A partial waiver on the ACS data deposition requirements was granted by the editors.

Supporting Information Available: Position of the hydrogen-bond and positional constraints employed in the PI3Kγ HTVS. Virtual screening performance of the PI3Kγ benchmarking study with varying docking constraints. Dose response curves for representative PI3Kγ and



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PI3Kδ compounds. Molecular formula strings with respective dose response data (pIC50). Spectra for representative PI3Kγ and PI3Kδ compounds.

PDB ID Codes: 2CHZ, 2X38

Abbreviations Used: HTVS, high-throughput virtual screening HTS, high-throughput screening PI3Kγ, phosphoinositide-3-kinase gamma PI3Kδ, phosphoinositide-3-kinase delta PIP2, phosphatidylinositol 4,5-bisphosphate PIP3, phosphotidyl inositol (3,4,5)-triphosphate POSx1, the positional constraint used for the specificity pocket in PI3Kδ GRP1, GST-tagged pleckstrin homology domain PH, pleckstrin homology APC, Allophycocyanin HBx2, 2 of the 4 hydrogen-bond constraints for PI3Kγ HBx3, 3 of the 4 hydrogen-bond constraints for PI3Kγ HBx1, hydrogen bond constraint used for the hinge in PI3Kδ

Author Information † These authors contributed equally *To whom the correspondence should be addressed.


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Phone: 858-320-3375. E-mail: [email protected]



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Shaw, J. P.; Williams, R. L. The p110delta structure: mechanisms for selectivity and potency of new PI(3)K inhibitors. Nat. Chem. Biol. 2010, 6, 117-124. 24. Somoza, J. R.; Koditek, D.; Villasenor, A. G.; Novikov, N.; Wong, M. H.; Liclican, A.; Xing, W.; Lagpacan, L.; Wang, R.; Schultz, B. E.; Papalia, G. A.; Samuel, D.; Lad, L.; McGrath, M. E. Structural, biochemical, and biophysical characterization of idelalisib binding to phosphoinositide 3-kinase delta. J. Biol. Chem. 2015, 290, 8439-8446. 25. Yang, Q.; Modi, P.; Newcomb, T.; Queva, C.; Gandhi, V. Idelalisib: First-in-Class PI3K Delta Inhibitor for the Treatment of Chronic Lymphocytic Leukemia, Small Lymphocytic Leukemia, and Follicular Lymphoma. Clin. Cancer Res. 2015, 21, 1537-1542. 26. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 2004, 47, 1739-1749. 27. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 2004, 47, 1750-1759. 28. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking. J. Med. Chem. 2006, 49, 6789-6801.



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Table 1: The confirmation hit rates of HTVS predicted compounds at varying levels of % inhibition compared to HTS results (10 µM concentration) for PI3Kγ. The number in parenthesis represents the total number of compounds from HTVS and HTS and the actual number of compounds identified at each level of % inhibition. The HTVS results presented in first column are a combination of two virtual screens: 1) P-HBx2- satisfying 2 of 4 hydrogen-bond constraints; GlideScore cutoff < -8 and 2) P-HBx3- satisfying 3 of 4 hydrogen-bond constraints; GlideScore cutoff < -7. To demonstrate their individual performance, P-HBx2, P-HBx3, and the intersection of the P-HBx2 and P-HBx3 hit list (P-HBx2∩P-HBx3) are also shown.

% Inhibition 50 60 70 80 90

HTVS (1,478) 10.3% (152) 6.8% (100) 4.7% (69) 2.7% (40) 1.8% (27)

HTS (58,371) 0.2% (134) 0.2% (98) 0.1% (82) 0.1% (60) 0.1% (46)

P-HBx2 (1,146) 10.2% (117) 6.9% (79) 4.6% (53) 2.7% (31) 1.8% (21)

P-HBx3 (538) 12.4% (67) 7.8% (42) 5.6% (30) 2.6% (14) 1.7% (9)


P-HBx2∩P-HBx3 (206) 15.5% (32) 10.2% (21) 6.8% (14) 2.4% (5) 1.5% (3)

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Table 2: Percentage of compounds with Tanimoto coefficients over a range of cutoffs. The compounds were identified from the PI3Kγ and PI3Kδ virtual screens by taking 1) all compounds with ≥ 50% inhibition (at 10µM) and 2) all compounds with pIC50 ≥ 5. The Tanimoto values were calculated using a nxn matrix (i.e. all compounds from set compared to one another). The number in parenthesis represents the total number of compounds for the respective set.

Tanimoto cut-off

A Prospective Virtual Screening Study: Enriching Hit Rates and

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