Application of Mechanistic Multiparameter Optimization and Large-Scale In Vitro to In Vivo Pharmacokinetics Correlations to Small-Molecule Therapeutic Projects.

Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

Application of empirical scalars to enable early prediction of human hepatic clearance using IVIVE in drug discovery: an evaluation of 173 drugs.

The utilization of in vitro data to predict drug pharmacokinetics (PK) in vivo has been a consistent practice in early drug discovery for decades. However, its success is hampered by mispredictions attributed to uncharacterized biological phenomena/experimental artifacts. Predicted drug clearance (CL) from experimental data (i.e. hepatocyte intrinsic clearance: CLint, fraction unbound in plasma: fu,p) is often systematically underpredicted using the well-stirred model (WSM). The objective of this study was to evaluate using empirical scalars in the WSM to correct for CL mispredictions. Drugs (N=28) were used to generate numerical scalars on CLint (α), and fu,p (ß) to minimize the error (AAFE) for CL predictions. These scalars were validated using an additional dataset (N=28 drugs) and applied to a non-redundant AstraZeneca (AZ) dataset available in the literature (N=117 drugs) for a total of 173 compounds. CL predictions using the WSM were improved for most compounds using an α value of 3.66 (~64%<2-fold) compared to no scaling (~46%<2-fold). Similarly, using a ß value of 0.55 or combination of α and ß scalars (values of 1.74 and 0.66, respectively) resulted in a similar improvement in predictions (~64%<2-fold and ~65%<2-fold, respectively). For highly bound compounds (fu,p{less than or equal to}0.01), AAFE was substantially reduced across all scaling methods. Using the ß scalar alone or a combination of α and ß appeared optimal; and produce larger magnitude corrections for highly-bound compounds. Some drugs are still disproportionally mispredicted, however the improvements in prediction error and simplicity of applying these scalars suggests its utility for early-stage CL predictions. Significance Statement In early drug discovery, prediction of human clearance using in vitro experimental data plays an essential role in triaging compounds prior to in vivo studies. These predictions have been systematically underestimated. Here we introduce empirical scalars calibrated on the extent of plasma protein binding that appear to improve clearance prediction across multiple datasets. This approach can be used in early phases of drug discovery prior to the availability of pre-clinical data for early quantitative predictions of human clearance.

Evaluation of Tissue Binding in Three Tissues across Five Species and Prediction of Volume of Distribution from Plasma Protein and Tissue Binding with an Existing Model.

Volume of distribution (Vd) is a primary pharmacokinetic parameter used to calculate the half-life and plasma concentration-time profile of drugs. Numerous models have been relatively successful in predicting Vd, but the model developed by Korzekwa and Nagar is of particular interest because it utilizes plasma protein binding and microsomal binding data, both of which are readily available in vitro parameters. Here, Korzekwa and Nagar's model was validated and expanded upon using external and internal data sets. Tissue binding, plasma protein binding, Vd, physiochemical, and physiologic data sets were procured from literature and Genentech's internal data base. First, we investigated the hypothesis that tissue binding is primarily governed by passive processes that depend on the lipid composition of the tissue type. The fraction unbound in tissues (futissue) was very similar across human, rat, and mouse. In addition, we showed that dilution factors could be generated from nonlinear regression so that one futissue value could be used to estimate another one regardless of species. More importantly, results suggested that microsomes could serve as a surrogate for tissue binding. We applied the parameters from Korzekwa and Nagar's Vd model to two distinct liver microsomal data sets and found remarkably close statistical results. Brain and lung data sets also accurately predicted Vd, further validating the model. Vd prediction accuracy for compounds with log D7.4 > 1 significantly outperformed that of more hydrophilic compounds. Finally, human Vd predictions from Korzekwa and Nagar's model appear to be as accurate as rat allometry and slightly less accurate than dog and cynomolgus allometry. SIGNIFICANCE STATEMENT: This study shows that tissue binding is comparable across five species and can be interconverted with a dilution factor. In addition, we applied internal and external data sets to the volume of distribution model developed by Korzekwa and Nagar and found comparable Vd prediction accuracy between the Vd model and single-species allometry. These findings could potentially accelerate the drug research and development process by reducing the amount of resources associated with in vitro binding and animal experiments.

Advances in the study of drug metabolism - symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX).

The 12th International Society for the Study of Xenobiotics (ISSX) meeting, held in Portland, OR, USA from July 28 to 31, 2019, was attended by diverse members of the pharmaceutical sciences community. The ISSX New Investigators Group provides learning and professional growth opportunities for student and early career members of ISSX. To share meeting content with those who were unable to attend, the ISSX New Investigators herein elected to highlight the "Advances in the Study of Drug Metabolism" symposium, as it engaged attendees with diverse backgrounds. This session covered a wide range of current topics in drug metabolism research including predicting sites and routes of metabolism, metabolite identification, ligand docking, and medicinal and natural products chemistry, and highlighted approaches complemented by computational modeling. In silico tools have been increasingly applied in both academic and industrial settings, alongside traditional and evolving in vitro techniques, to strengthen and streamline pharmaceutical research. Approaches such as quantum mechanics simulations facilitate understanding of reaction energetics toward prediction of routes and sites of drug metabolism. Furthermore, in tandem with crystallographic and orthogonal wet lab techniques for structural validation of drug metabolizing enzymes, in silico models can aid understanding of substrate recognition by particular enzymes, identify metabolic soft spots and predict toxic metabolites for improved molecular design. Of note, integration of chemical synthesis and biosynthesis using natural products remains an important approach for identifying new chemical scaffolds in drug discovery. These subjects, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are discussed in this review.

Computational analysis of kinase inhibitor selectivity using structural knowledge.

Motivation: Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling. Results: Here, we present a KinomeFEATURE database for kinase binding site similarity search by comparing protein microenvironments characterized using diverse physiochemical descriptors. Initial selectivity prediction of 15 known kinase inhibitors achieved an >90% accuracy and demonstrated improved performance in comparison to commonly used kinase inhibitor selectivity prediction methods. Additional kinase ATP binding site similarity assessment (120 binding sites) identified 55 kinases with significant promiscuity and revealed unexpected inhibitor cross-activities between PKR and FGFR2 kinases. Kinome-wide selectivity profiling of 11 kinase drug candidates predicted novel as well as experimentally validated off-targets and suggested structural mechanisms of kinase cross-activities. Our study demonstrated potential utilities of our approach for large-scale kinase inhibitor selectivity profiling that could contribute to kinase drug development and safety assessment. Availability and implementation: The KinomeFEATURE database and the associated scripts for performing kinase pocket similarity search can be downloaded from the Stanford SimTK website (https://simtk.org/projects/kdb). Supplementary information: Supplementary data are available at Bioinformatics online.

Evaluating the Utility of Canine Mdr1 Knockout Madin-Darby Canine Kidney I Cells in Permeability Screening and Efflux Substrate Determination.

Permeability assays are commonly conducted with Madin-Darby canine kidney (MDCK) cells to predict the intestinal absorption of small-molecule drug candidates. In addition, MDCK cells transfected to overexpress efflux transporters are often used to identify substrates. However, MDCK cells exhibit endogenous efflux activity for a significant proportion of experimental compounds, potentially leading to the underestimation of permeability and confounded findings in transport studies. The goal of this study was to evaluate canine Mdr1 knockout MDCK (gMDCKI) cells in permeability screening and human MDR1 substrate determination in a drug discovery setting. The gMDCKI cells were established by CRISPR-Cas9-mediated knockout of the canine Mdr1 gene in MDCKI wildtype (wt) cells. A comparison of efflux ratios (ER) between MDCKI wt and gMDCKI showed that out of 135 compounds tested, 38% showed efflux activity in MDCKI wt, while no significant efflux was observed in gMDCKI cells. Apparent permeability (Papp) from apical-to-basolateral (A-to-B) and basolateral-to-apical were near unity in gMDCKI cells, which approximated passive permeability, and 17% of compounds demonstrated increases in their Papp A-to-B values. Overexpression of human MDR1 in gMDCKI (gMDCKI-MDR1) cells enabled substrate determination without the contribution of endogenous efflux, and the assay was able to deconvolute ambiguous results from MDCKI-MDR1 and identify species differences in substrate specificity. An analysis of 395 and 474 compounds in gMDCKI and gMDCKI-MDR1, respectively, suggested physicochemical properties that were associated with low permeability correlated with MDR1 recognition. Poorly permeable compounds and MDR1 substrates were more likely to be large, flexible, and more capable of forming external hydrogen bonds. On the basis of our evaluation, we concluded that gMDCKI is a better cell line for permeability screening and efflux substrate determination than the MDCK wt cell line.

Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs.

PURPOSE: Volume of distribution at steady state (Vdss) is a fundamental pharmacokinetic (PK) parameter driven predominantly by passive processes and physicochemical properties of the compound. Human Vdss can be estimated using in silico mechanistic methods or empirically scaled from Vdss values obtained from preclinical species. In this study the accuracy and the complementarity of these two approaches are analyzed leveraging a large data set (over 150 marketed drugs). METHODS: For all the drugs analyzed in this study experimental in vitro measurements of LogP, plasma protein binding and pKa are used as input for the mechanistic in silico model to predict human Vdss. The software used for predicting human tissue partition coefficients and Vdss based on the method described by Rodgers and Rowland is made available as supporting information. RESULTS: This assessment indicates that overall the in silico mechanistic model presented by Rodgers and Rowland is comparably accurate or superior to empirical approaches based on the extrapolation of in vivo data from preclinical species. CONCLUSIONS: These results illustrate the great potential of mechanistic in silico models to accurately predict Vdss in humans. This in silico method does not rely on in vivo data and is, consequently, significantly time and resource sparing. The success of this in silico model further suggests that reasonable predictability of Vdss in preclinical species could be obtained by a similar process.

Design, synthesis, and biological evaluation of pyrrolobenzodiazepine-containing hypoxia-activated prodrugs.

The ability of various pyrrolobenzodiazepine(PBD)-containing cytotoxic compounds to function as hypoxia-activated prodrugs was assessed. These molecules incorporated a 1-methyl-2-nitro-1H-imidazole hypoxia-activated trigger (present in the clinically evaluated compound TH-302) in a manner that masked a reactive imine moiety required for cytotoxic activity. Incubation of the prodrugs with cytochrome P450-reductase under normoxic and hypoxic conditions revealed that some, but not all, were efficient substrates for the enzyme. In these experiments, prodrugs derived from PBD-monomers underwent rapid conversion to the parent cytotoxic compounds under low-oxygen conditions while related PBD-dimers did not. The ability of a given prodrug to function as an efficient cytochrome P450-reductase substrate correlated with the ratio of cytotoxic potencies measured for the compound against NCI460 cells under normoxic and hypoxic conditions.

Best of both worlds: on the complementarity of ligand-based and structure-based virtual screening.

Virtual screening with docking is an integral component of drug design, particularly during hit finding phases. While successful prospective studies of virtual screening exist, it remains a significant challenge to identify best practices a priori due to the many factors that influence the final outcome, including targets, data sets, software, metrics, and expert knowledge of the users. This study investigates the extent to which ligand-based methods can be applied to improve structure-based methods. The use of ligand-based methods to modulate the number of hits identified using the protein-ligand complex and also the diversity of these hits from the crystallographic ligand is discussed. In this study, 40 CDK2 ligand complexes were used together with two external data sets containing both actives and inactives from GlaxoSmithKline (GSK) and actives and decoys from the Directory of Useful Decoys (DUD). Results show how ligand-based modeling can be used to select a more appropriate protein conformation for docking, as well as to assess the reliability of the docking experiment. The time gained by reducing the pool of virtual screening candidates via ligand-based similarity can be invested in more accurate docking procedures, as well as in downstream labor-intensive approaches (e.g., visual inspection) maximizing the use of the chemical and biological information available. This provides a framework for molecular modeling scientists that are involved in initiating virtual screening campaigns with practical advice to make best use of the information available to them.

Balanced Permeability Index: A Multiparameter Index for Improved In Vitro Permeability.

The optimization of passive permeability is a key objective for orally available small molecule drug candidates. For drugs targeting the central nervous system (CNS), minimizing P-gp-mediated efflux is an additional important target for optimization. The physicochemical properties most strongly associated with high passive permeability and lower P-gp efflux are size, polarity, and lipophilicity. In this study, a new metric called the Balanced Permeability Index (BPI) was developed that combines these three properties. The BPI was found to be more effective than any single property in classifying molecules based on their permeability and efflux across a diverse range of chemicals and assays. BPI is easy to understand, allowing researchers to make decisions about which properties to prioritize during the drug development process.

PROTACs Targeting BRM (SMARCA2) Afford Selective In Vivo Degradation over BRG1 (SMARCA4) and Are Active in BRG1 Mutant Xenograft Tumor Models.

The identification of VHL-binding proteolysis targeting chimeras (PROTACs) that potently degrade the BRM protein (also known as SMARCA2) in SW1573 cell-based experiments is described. These molecules exhibit between 10- and 100-fold degradation selectivity for BRM over the closely related paralog protein BRG1 (SMARCA4). They also selectively impair the proliferation of the H1944 "BRG1-mutant" NSCLC cell line, which lacks functional BRG1 protein and is thus highly dependent on BRM for growth, relative to the wild-type Calu6 line. In vivo experiments performed with a subset of compounds identified PROTACs that potently and selectively degraded BRM in the Calu6 and/or the HCC2302 BRG1 mutant NSCLC xenograft models and also afforded antitumor efficacy in the latter system. Subsequent PK/PD analysis established a need to achieve strong BRM degradation (>95%) in order to trigger meaningful antitumor activity in vivo. Intratumor quantitation of mRNA associated with two genes whose transcription was controlled by BRM (PLAU and KRT80) also supported this conclusion.

Optimization of a Novel DEL Hit That Binds in the Cbl-b SH2 Domain and Blocks Substrate Binding.

We were attracted to the therapeutic potential of inhibiting Casitas B-lineage lymphoma proto-oncogene-b (Cbl-b), a RING E3 ligase that plays a critical role in regulating the activation of T cells. However, given that only protein-protein interactions were involved, it was unclear whether inhibition by a small molecule would be a viable approach. After screening an ∼6 billion member DNA-encoded library (DEL) using activated Cbl-b, we identified compound 1 as a hit for which the cis-isomer (2) was confirmed by biochemical and surface plasmon resonance (SPR) assays. Our hit optimization effort was greatly accelerated when we obtained a cocrystal structure of 2 with Cbl-b, which demonstrated induced binding at the substrate binding site, namely, the Src homology-2 (SH2) domain. This was quite noteworthy given that there are few reports of small molecule inhibitors that bind to SH2 domains and block protein-protein interactions. Structure- and property-guided optimization led to compound 27, which demonstrated measurable cell activity, albeit only at high concentrations.

Development of Liposome Systems for Enhancing the PK Properties of Bivalent PROTACs.

Proteolysis-Targeting Chimeras (PROTACs) are a promising new technology in drug development. They have rapidly evolved in recent years, with several of them in clinical trials. While most of these advances have been associated with monovalent protein degraders, bivalent PROTACs have also entered clinical trials, although progression to market has been limited. One of the reasons is the complex physicochemical properties of the heterobifunctional PROTACs. A promising strategy to improve pharmacokinetics of highly lipophilic compounds, such as PROTACs, is encapsulation in liposome systems. Here we describe liposome systems for intravenous administration to enhance the PK properties of two bivalent PROTAC molecules, by reducing clearance and increasing systemic coverage. We developed and characterized a PROTAC-in-cyclodextrin liposome system where the drug was retained in the liposome core. In PK studies at 1 mg/kg for GNE-01 the PROTAC-in-cyclodextrin liposome, compared to the solution formulation, showed a 80- and a 380-fold enhancement in AUC for mouse and rat studies, respectively. We further investigated the same PROTAC-in-cyclodextrin liposome system with the second PROTAC (GNE-02), where we monitored both lipid and drug concentrations in vivo. Similarly, in a mouse PK study of GEN-02, the PROTAC-in-cyclodextrin liposome system exhibited enhancement in plasma concentration of a 23× increase over the conventional solution formulation. Importantly, the lipid CL correlated with the drug CL. Additionally, we investigated a conventional liposome approach for GNE-02, where the PROTAC resides in the lipid bilayer. Here, a 5× increase in AUC was observed, compared to the conventional solution formulation, and the drug CL was faster than the lipid CL. These results indicate that the different liposome systems can be tailored to translate across multiple PROTAC systems to modulate and improve plasma concentrations. Optimization of the liposomes could further improve tumor concentration and improve the overall therapeutic index (TI). This delivery technology may be well suited to bring novel protein targeted PROTACs into clinics.

BDDCS class prediction for new molecular entities.

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

QSAR modeling and data mining link Torsades de Pointes risk to the interplay of extent of metabolism, active transport, and HERG liability.

We collected 1173 hERG patch clamp (PC) data (IC50) from the literature to derive twelve classification models for hERG inhibition, covering a large variety of chemical descriptors and classification algorithms. Models were generated using 545 molecules and validated through 258 external molecules tested in PC experiments. We also evaluated the suitability of the best models to predict the activity of 26 proprietary compounds tested in radioligand binding displacement (RBD). Results proved the necessity to use multiple validation sets for a true estimation of model accuracy and demonstrated that using various descriptors and algorithms improves the performance of ligand-based models. Intriguingly, one of the most accurate models uncovered an unexpected link between extent of metabolism and hERG liability. This hypothesis was fairly reinforced by using the Biopharmaceutics Drug Disposition Classification System (BDDCS) that recognized 94% of the hERG inhibitors as extensively metabolized in vivo. Data mining suggested that high Torsades de Pointes (TdP) risk results from an interplay of hERG inhibition, extent of metabolism, active transport, and possibly solubility. Overall, these new findings might improve both the decision making skills of pharmaceutical scientists to mitigate hERG liability during the drug discovery process and the TdP risk assessment during drug development.

QSAR models for P-glycoprotein transport based on a highly consistent data set.

P-Glycoprotein (Pgp) is involved in the elimination and in the disposition of a significant portion of marketed drugs. So far, publicly available data sets used for modeling Pgp transport included compounds tested in different assays, different cell lines, and different protocols. In this work, we present a collection of 478 Efflux Ratios (ERs) in MDCK-MDR1 cell lines, and from this collection we define a data set of 187 compounds that were tested in the Borst-derived MDCK-MDR1 cell lines. Of the 23 models resulting from the use of different descriptors, classification algorithms, and variable selection techniques, the 4 most accurate in external validation (∼0.86) are based on VolSurf+ (VS+) descriptors. Two of these models are Naïve Bayes (NB) classifiers using 4 descriptors that were selected through a new technique hereby first time extensively described.

When Cofactors Aren't X Factors: Functional Groups That Are Labile in Human Liver Microsomes in the Absence of NADPH.

The metabolic stability of compounds is often assessed at an early stage in drug discovery programs by profiling with hepatic microsomes. Exclusion of the reduced form of nicotinamide adenine dinucleotide phosphate (NADPH) in these assays provides insight into non-cytochrome P450 (CYP)-mediated metabolism. This report uses a matched molecular pair (MMP) application to assess which chemical substituents are commonly susceptible to non-NADPH-mediated metabolism by microsomes. The analysis found the overall prevalence of metabolism in the absence of NADPH to be low, with esters, amides, aldehydes, and oxetanes being among the most commonly susceptible functional groups. Given that non-CYP enzymes, such as esterases, may be expressed extrahepatically and lead to lower confidence in predicted pharmacokinetic profiles, an awareness of the functional groups that commonly undergo non-NADPH-mediated metabolism-as well as options for their replacement based on experimental MMP data-may help researchers derisk metabolic stability issues at an earlier stage in drug discovery.

Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces.

In this work, we benchmark a variety of single- and multi-task graph neural network (GNN) models against lower-bar and higher-bar traditional machine learning approaches employing human engineered molecular features. We consider four GNN variants - Graph Convolutional Network (GCN), Graph Attention Network (GAT), Message Passing Neural Network (MPNN), and Attentive Fingerprint (AttentiveFP). So far deep learning models have been primarily benchmarked using lower-bar traditional models solely based on fingerprints, while more realistic benchmarks employing fingerprints, whole-molecule descriptors and predictions from other related endpoints (e. g., LogD7.4) appear to be scarce for industrial ADME datasets. In addition to time-split test sets based on Genentech data, this study benefits from the availability of measurements from an external chemical space (Roche data). We identify GAT as a promising approach to implementing deep learning models. While all the deep learning models significantly outperform lower-bar benchmark traditional models solely based on fingerprints, only GATs seem to offer a small but consistent improvement over higher-bar benchmark traditional models. Finally, the accuracy of in vitro assays from different laboratories predicting the same experimental endpoints appears to be comparable with the accuracy of GAT single-task models, suggesting that most of the observed error from the models is a function of the experimental error propagation.

Discovering cell-active BCL6 inhibitors: effectively combining biochemical HTS with multiple biophysical techniques, X-ray crystallography and cell-based assays.

By suppressing gene transcription through the recruitment of corepressor proteins, B-cell lymphoma 6 (BCL6) protein controls a transcriptional network required for the formation and maintenance of B-cell germinal centres. As BCL6 deregulation is implicated in the development of Diffuse Large B-Cell Lymphoma, we sought to discover novel small molecule inhibitors that disrupt the BCL6-corepressor protein-protein interaction (PPI). Here we report our hit finding and compound optimisation strategies, which provide insight into the multi-faceted orthogonal approaches that are needed to tackle this challenging PPI with small molecule inhibitors. Using a 1536-well plate fluorescence polarisation high throughput screen we identified multiple hit series, which were followed up by hit confirmation using a thermal shift assay, surface plasmon resonance and ligand-observed NMR. We determined X-ray structures of BCL6 bound to compounds from nine different series, enabling a structure-based drug design approach to improve their weak biochemical potency. We developed a time-resolved fluorescence energy transfer biochemical assay and a nano bioluminescence resonance energy transfer cellular assay to monitor cellular activity during compound optimisation. This workflow led to the discovery of novel inhibitors with respective biochemical and cellular potencies (IC50s) in the sub-micromolar and low micromolar range.