Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening.

High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.

Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening.

Despite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired t-test p-values <10(-15). A per-assay assessment showed that the BEDROC(α = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives.

Trisubstituted-Imidazoles Induce Apoptosis in Human Breast Cancer Cells by Targeting the Oncogenic PI3K/Akt/mTOR Signaling Pathway.

Overactivation of PI3K/Akt/mTOR is linked with carcinogenesis and serves a potential molecular therapeutic target in treatment of various cancers. Herein, we report the synthesis of trisubstituted-imidazoles and identified 2-chloro-3-(4, 5-diphenyl-1H-imidazol-2-yl) pyridine (CIP) as lead cytotoxic agent. Naïve Base classifier model of in silico target prediction revealed that CIP targets RAC-beta serine/threonine-protein kinase which comprises the Akt. Furthermore, CIP downregulated the phosphorylation of Akt, PDK and mTOR proteins and decreased expression of cyclin D1, Bcl-2, survivin, VEGF, procaspase-3 and increased cleavage of PARP. In addition, CIP significantly downregulated the CXCL12 induced motility of breast cancer cells and molecular docking calculations revealed that all compounds bind to Akt2 kinase with high docking scores compared to the library of previously reported Akt2 inhibitors. In summary, we report the synthesis and biological evaluation of imidazoles that induce apoptosis in breast cancer cells by negatively regulating PI3K/Akt/mTOR signaling pathway.

Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data.

With increased automation and larger compound collections, the development of high-throughput screening (HTS) started replacing previous approaches in drug discovery from around the 1980s onward. However, even today it is not always appropriate, or even feasible, to screen large collections of compounds in a particular assay. Here, we present an efficient method for iterative screening of small subsets of compound libraries. With this method, the retrieval of active compounds is optimized using their structural information and biological activity fingerprints. We validated this approach retrospectively on 34 Novartis in-house HTS assays covering a wide range of assay biology, including cell proliferation, antibacterial activity, gene expression, and phosphorylation. This method was employed to retrieve subsets of compounds for screening, where selected hits from any given round of screening were used as starting points to select chemically and biologically similar compounds for the next iteration. By only screening ∼1% of the full screening collection (∼15 000 compounds), the method consistently retrieves diverse compounds belonging to the top 0.5% of the most active compounds for the HTS campaign. For most of the assays, over half of the compounds selected by the method were found to be among the 5% most active compounds of the corresponding full-deck HTS. In addition, the stringency of the iterative method can be modified depending on the number of compounds one can afford to screen, making it a flexible tool to discover active compounds efficiently.

Novel Adamantanyl-Based Thiadiazolyl Pyrazoles Targeting EGFR in Triple-Negative Breast Cancer.

The epidermal growth factor receptor (EGFR) is a validated therapeutic target for triple-negative breast cancer (TNBC). In the present study, we synthesize novel adamantanyl-based thiadiazolyl pyrazoles by introducing the adamantane ring to thiazolopyrazoline. On the basis of loss of cell viability in TNBC cells, 4-(adamantan-1-yl)-2-(3-(2,4-dichlorophenyl)-5-phenyl-4,5-dihydro-1H-pyrazol-1-yl)thiazole (APP) was identified as a lead compound. Using a Parzen-Rosenblatt Window classifier, APP was predicted to target the EGFR protein, and the same was confirmed by surface plasmon resonance. Further analysis revealed that APP suppressed the phosphorylation of EGFR at Y992, Y1045, Y1068, Y1086, Y1148, and Y1173 in TNBC cells. APP also inhibited the phosphorylation of ERK at Y204 and of STAT3 at Y705, implying that APP downregulates the activity of EGFR downstream effectors. Small interfering RNA mediated depletion of EGFR expression prevented the effect of APP in BT549 and MDA-MB-231 cells, indicating that APP specifically targets the EGFR. Furthermore, APP modulated the expression of the proteins involved in cell proliferation and survival. In addition, APP altered the expression of epithelial-mesenchymal transition related proteins and suppressed the invasion of TNBC cells. Hence, we report a novel and specific inhibitor of the EGFR signaling cascade.

A Nano-MgO and Ionic Liquid-Catalyzed 'Green' Synthesis Protocol for the Development of Adamantyl-Imidazolo-Thiadiazoles as Anti-Tuberculosis Agents Targeting Sterol 14α-Demethylase (CYP51).

In this work, we describe the 'green' synthesis of novel 6-(adamantan-1-yl)-2-substituted-imidazo[2,1-b][1,3,4]thiadiazoles (AITs) by ring formation reactions using 1-(adamantan-1-yl)-2-bromoethanone and 5-alkyl/aryl-2-amino1,3,4-thiadiazoles on a nano material base in ionic liquid media. Given the established activity of imidazothiadiazoles against M. tuberculosis, we next examined the anti-TB activity of AITs against the H37Rv strain using Alamar blue assay. Among the tested compounds 6-(adamantan-1-yl)-2-(4-methoxyphenyl)imidazo[2,1-b][1,3,4]thiadiazole (3f) showed potent inhibitory activity towards M. tuberculosis with an MIC value of 8.5 µM. The inhibitory effect of this molecule against M. tuberculosis was comparable to the standard drugs such as Pyrazinamide, Streptomycin, and Ciprofloxacin drugs. Mechanistically, an in silico analysis predicted sterol 14α-demethylase (CYP51) as the likely target and experimental activity of 3f in this system corroborated the in silico target prediction. In summary, we herein report the synthesis and biological evaluation of novel AITs against M. tuberculosis that likely target CYP51 to induce their antimycobacterial activity.

Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules.

The rampant increase of public bioactivity databases has fostered the development of computational chemogenomics methodologies to evaluate potential ligand-target interactions (polypharmacology) both in a qualitative and quantitative way. Bayesian target prediction algorithms predict the probability of an interaction between a compound and a panel of targets, thus assessing compound polypharmacology qualitatively, whereas structure-activity relationship techniques are able to provide quantitative bioactivity predictions. We propose an integrated drug discovery pipeline combining in silico target prediction and proteochemometric modelling (PCM) for the respective prediction of compound polypharmacology and potency/affinity. The proposed pipeline was evaluated on the retrospective discovery of Plasmodium falciparum DHFR inhibitors. The qualitative in silico target prediction model comprised 553,084 ligand-target associations (a total of 262,174 compounds), covering 3,481 protein targets and used protein domain annotations to extrapolate predictions across species. The prediction of bioactivities for plasmodial DHFR led to a recall value of 79% and a precision of 100%, where the latter high value arises from the structural similarity of plasmodial DHFR inhibitors and T. gondii DHFR inhibitors in the training set. Quantitative PCM models were then trained on a dataset comprising 20 eukaryotic, protozoan and bacterial DHFR sequences, and 1,505 distinct compounds (in total 3,099 data points). The most predictive PCM model exhibited R (2) 0 test and RMSEtest values of 0.79 and 0.59 pIC50 units respectively, which was shown to outperform models based exclusively on compound (R (2) 0 test/RMSEtest = 0.63/0.78) and target information (R (2) 0 test/RMSEtest = 0.09/1.22), as well as inductive transfer knowledge between targets, with respective R (2) 0 test and RMSEtest values of 0.76 and 0.63 pIC50 units. Finally, both methods were integrated to predict the protein targets and the potency on plasmodial DHFR for the GSK TCAMS dataset, which comprises 13,533 compounds displaying strong anti-malarial activity. 534 of those compounds were identified as DHFR inhibitors by the target prediction algorithm, while the PCM algorithm identified 25 compounds, and 23 compounds (predicted pIC50 > 7) were identified by both methods. Overall, this integrated approach simultaneously provides target and potency/affinity predictions for small molecules. Graphical abstractProteochemometric modelling coupled to in silico target prediction.

Novel synthetic coumarins that targets NF-κB in Hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the fifth most common malignant tumor worldwide, and is the third most common cause of cancer related death. Constitutive activation of NF-κB is the underlying mechanism behind tumorigenesis and this protein regulates the expression of genes involved in proliferation, survival, drug resistance, angiogenesis and metastasis. The design of inhibitors which suppress NF-κB activation is therefore of great therapeutic importance in the treatment of HCC. In this study, we investigated the effect of newly synthesized coumarin derivatives against HCC cells, and identified (7-Carbethoxyamino-2-oxo-2H-chromen-4-yl)methylpyrrolidine-1 carbodithioate (CPP) as lead compound. Further, we evaluated the effect of CPP on the DNA binding ability of NF-κB, CXCL12-induced cell migration and invasion, and the regulated gene products in HCC cells. We found that CPP induced cytotoxicity in three HCC cells in a time and dose dependent manner, and suppressed the DNA binding ability of NF-κB. CPP significantly decreased the CXCL12-induced cell migration and invasion. More evidently, CPP inhibits the expression of NF-κB targeted genes such as cyclin D1, Bcl-2, survivin, MMP12 and C-Myc. Furthermore, the molecular docking analysis suggested that CPP interacts with the p50 binding domain of the p65 subunit, scoring best among the 26 docked coumarin derivatives of this study. Thus, we are reporting CPP as a potent inhibitor of the pro-inflammatory pathway in Hepatocellular carcinoma.

Novel benzoxazine-based aglycones block glucose uptake in vivo by inhibiting glycosidases.

Glycoside hydrolases catalyze the selective hydrolysis of glycosidic bonds in oligosaccharides, polysaccharides, and their conjugates. ß-glucosidases occur in all domains of living organisms and constitute a major group among glycoside hydrolases. On the other hand, the benzoxazinoids occur in living systems and act as stable ß-glucosides, such as 2-(2,4-dihydroxy-7-methoxy-2H-1,4-benzoxazin-3(4H)-one)-ß-D-gluco-pyranose, which hydrolyse to an aglycone DIMBOA. Here, we synthesized the library of novel 1,3-benzoxazine scaffold based aglycones by using 2-aminobenzyl alcohols and aldehydes from one-pot reaction in a chloroacetic acid catalytic system via aerobic oxidative synthesis. Among the synthesized benzoxazines, 4-(7-chloro-2,4-dihydro-1H-benzo[d][1,3]oxazin-2-yl)phenol (compound 7) exhibit significant inhibition towards glucosidase compared to acarbose, with a IC50 value of 11.5 µM. Based upon results generated by in silico target prediction algorithms (Naïve Bayesian classifier), these aglycones potentially target the additional sodium/glucose cotransporter 1 (where a log likelihood score of 2.70 was observed). Furthermore, the in vitro glucosidase activity was correlated with the in silico docking results, with a high docking score for the aglycones towards the substrate binding site of glycosidase. Evidently, the in vitro and in vivo experiments clearly suggest an anti-hyperglycemic effect via glucose uptake inhibition by 4-(7-chloro-2,4-dihydro-1H-benzo[d][1,3]oxazin-2-yl)phenol in the starved rat model. These synthetic aglycones could constitute a novel pharmacological approach for the treatment, or re-enforcement of existing treatments, of type 2 diabetes and associated secondary complications.

How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space.

Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, "Bayes Affinity Fingerprints"). We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior. We then applied eight of the molecular descriptors compared in this study to sample a diverse subset of sample compounds (4%) from an initial population of 2587 compounds, covering the 25 largest human activity classes from ChEMBL and measured the coverage of activity classes by the subsets. Here, it was found that "Bayes Affinity Fingerprints" achieved an average coverage of 92% of activity classes. Using the descriptors ECFP4, GpiDAPH3, TGT, and random sampling, 91%, 84%, 84%, and 84% of the activity classes were represented in the selected compounds respectively, followed by BCUT, prop2D, MACCS, and PMI (in order of decreasing performance). In addition, we were able to show that there is no visible correlation between compound diversity in PMI space and in bioactivity space, despite frequent utilization of PMI plots to this end. To summarize, in this work, we assessed which descriptors select compounds with high coverage of bioactivity space, and can hence be used for diverse compound selection for biological screening. In cases where multiple descriptors are to be used for diversity selection, this work describes which descriptors behave complementarily, and can hence be used jointly to focus on different aspects of diversity in chemical space.

Are phylogenetic trees suitable for chemogenomics analyses of bioactivity data sets: the importance of shared active compounds and choosing a suitable data embedding method, as exemplified on Kinases.

BACKGROUND: 'Phylogenetic trees' are commonly used for the analysis of chemogenomics datasets and to relate protein targets to each other, based on the (shared) bioactivities of their ligands. However, no real assessment as to the suitability of this representation has been performed yet in this area. We aimed to address this shortcoming in the current work, as exemplified by a kinase data set, given the importance of kinases in many diseases as well as the availability of large-scale datasets for analysis. In this work, we analyzed a dataset comprising 157 compounds, which have been tested at concentrations of 1 µM and 10 µM against a panel of 225 human protein kinases in full-matrix experiments, aiming to explain kinase promiscuity and selectivity against inhibitors. Compounds were described by chemical features, which were used to represent kinases (i.e. each kinase had an active set of features and an inactive set). RESULTS: Using this representation, a bioactivity-based classification was made of the kinome, which partially resembles previous sequence-based classifications, where particularly kinases from the TK, CDK, CLK and AGC branches cluster together. However, we were also able to show that in approximately 57% of cases, on average 6 kinase inhibitors exhibit activity against kinases which are located at a large distance in the sequence-based classification (at a relative distance of 0.6 - 0.8 on a scale from 0 to 1), but are correctly located closer to each other in our bioactivity-based tree (distance 0 - 0.4). Despite this improvement on sequence-based classification, also the bioactivity-based classification needed further attention: for approximately 80% of all analyzed kinases, kinases classified as neighbors according to the bioactivity-based classification also show high SAR similarity (i.e. a high fraction of shared active compounds and therefore, interaction with similar inhibitors). However, in the remaining ~20% of cases a clear relationship between kinase bioactivity profile similarity and shared active compounds could not be established, which is in agreement with previously published atypical SAR (such as for LCK, FGFR1, AKT2, DAPK1, TGFR1, MK12 and AKT1). CONCLUSIONS: In this work we were hence able to show that (1) targets (here kinases) with few shared activities are difficult to establish neighborhood relationships for, and (2) phylogenetic tree representations make implicit assumptions (i.e. that neighboring kinases exhibit similar interaction profiles with inhibitors) that are not always suitable for analyses of bioactivity space. While both points have been implicitly alluded to before, this is to the information of the authors the first study that explores both points on a comprehensive basis. Excluding kinases with few shared activities improved the situation greatly (the percentage of kinases for which no neighborhood relationship could be established dropped from 20% to only 4%). We can conclude that all of the above findings need to be taken into account when performing chemogenomics analyses, also for other target classes.