Kinase Inhibitor Data Modeling and de Novo Inhibitor Design with Fragment Approaches

Discovery of benzimidazole-indazole derivatives as potent FLT3-tyrosine kinase domain mutant kinase inhibitors for acute myeloid leukemia

The FMS-like tyrosine kinase 3 (FLT3) gene encodes a class III receptor tyrosine kinase that is expressed in hematopoietic stem cells. The mutations of FLT3 gene found in 30% of acute myeloid leukemia (AML), leads to an abnormal constitutive activation of FLT3 kinase of the receptor and results in immature myeloblast cell proliferation. Although small molecule drugs targeting the FLT3 kinase have been approved, new FLT3 inhibitors are needed owing to the side effects and drug resistances arising from kinase domain mutations, such as D835Y and F691L. In this study, we have developed benzimidazole-indazole based novel inhibitors targeting mutant FLT3 kinases through the optimization of diverse chemical moieties substituted around the core skeleton. The most optimized compound 22f exhibited potent inhibitory activities against FLT3 and FLT3/D835Y, with IC50 values of 0.941 and 0.199 nM, respectively. Furthermore, 22f exhibited strong antiproliferative activity against an AML cell line, MV4-11 cells with a GI50 of 0.26 nM. More importantly, 22f showed single-digit nanomolar GI50 values in the mutant FLT kinase expressed Ba/F3 cell lines including FLT-D835Y (GI50 = 0.29 nM) and FLT3-F691L (GI50 = 2.87 nM). Molecular docking studies indicated that the compound exhibits a well-fitted binding mode as a type 1 inhibitor in the homology model of active conformation of FLT3 kinase.

Rho-kinase (ROCK) Inhibitors - A Neuroprotective Therapeutic Paradigm with a Focus on Ocular Utility

BACKGROUND

Glaucoma is a progressive optic neuropathy causing visual impairment and Retinal Ganglionic Cells (RGCs) death gradually posing a need for neuroprotective strategies to minimize the loss of RGCs and visual field. It is recognized as a multifactorial disease, Intraocular Pressure (IOP) being the foremost risk factor. ROCK inhibitors have been probed for various possible indications, such as myocardial ischemia, hypertension, kidney diseases. Their role in neuroprotection and neuronal regeneration has been suggested to be of value in the treatment of neurological diseases, like spinal-cord injury, Alzheimer's disease and multiple sclerosis but recently Rho-associated Kinase inhibitors have been recognized as potential antiglaucoma agents.

EVIDENCE SYNTHESIS

Rho-Kinase is a serine/threonine kinase with a kinase domain which is constitutively active and is involved in the regulation of smooth muscle contraction and stress fibre formation. Two isoforms of Rho-Kinase, ROCK-I (ROCK β) and ROCK-II (ROCK α) have been identified. ROCK II plays a pathophysiological role in glaucoma and hence the inhibitors of ROCK may be beneficial to ameliorate the vision loss. These inhibitors decrease the intraocular pressure in the glaucomatous eye by increasing the aqueous humour outflow through the trabecular meshwork pathway. They also act as anti-scarring agents and hence prevent post-operative scarring after the glaucoma filtration surgery. Their major role involves axon regeneration by increasing the optic nerve blood flow which may be useful in treating the damaged optic neurons. These drugs act directly on the neurons in the central visual pathway, interrupting the RGC apoptosis and therefore serve as a novel pharmacological approach for glaucoma neuroprotection.

CONCLUSION

Based on the results of high-throughput screening, several Rho kinase inhibitors have been designed and developed comprising of diverse scaffolds exhibiting Rho kinase inhibitory activity from micromolar to subnanomolar ranges. This diversity in the scaffolds with inhibitory potential against the kinase and their SAR development will be intricated in the present review. Ripasudil is the only Rho kinase inhibitor marketed to date for the treatment of glaucoma. Another ROCK inhibitor AR-13324 has recently passed the clinical trials whereas AMA0076, K115, PG324, Y39983 and RKI-983 are still under trials. In view of this, a detailed and updated account of ROCK II inhibitors as the next generation therapeutic agents for glaucoma will be discussed in this review.

Creating a Virtual Assistant for Medicinal Chemistry

The virtual assistant concept is one that many technology companies have taken on despite having other well-developed and popular user interfaces. We wondered whether it would be possible to create an effective virtual assistant for a medicinal chemistry organization, the key being delivering the information the user would want to see, directly to them, at the right time. We introduce Kernel, an early prototype virtual assistant created at Lilly, and a number of examples of the scenarios that have been implemented to try to demonstrate the concept. A biochemical assay summary email is described that brings together new results and some basic analysis, delivered within an hour of new data appearing for that assay, and an email delivering new compound design ideas directly to the original submitter of a compound shortly after their compound was tested for the first time. We conclude with a high level description of the first example of a Design-Make-Test-Analyze cycle completed in the absence of any human intellectual input at Lilly. We believe that this concept has much potential in changing the way that computational results and analysis are delivered and consumed within a medicinal chemistry group, and we hope to inspire others to implement their own similar solutions.

Idea2Data: Toward a New Paradigm for Drug Discovery

Increasing the success rate and throughput of drug discovery will require efficiency improvements throughout the process that is currently used in the pharmaceutical community, including the crucial step of identifying hit compounds to act as drivers for subsequent optimization. Hit identification can be carried out through large compound collection screening and often involves the generation and testing of many hypotheses based on available knowledge. In practice, hypothesis generation can involve the selection of promising chemical structures from compound collections using predictive models built from previous screening/assay results. Available physical collections, typically used during hit identification, are of the order of 10⁶ compounds but represent only a small fraction of the small molecule drug-like chemical space. In an effort to survey a larger portion of chemical space and eliminate inefficiencies during hit identification, we introduce a new process, termed Idea2Data (I2D) that tightly integrates computational and experimental components of the drug discovery process. I2D provides the ability to connect a vast virtual collection of compounds readily synthesizable on automated synthesis systems with computational predictive models for the identification of promising structures. This new paradigm enables researchers to process billions of virtual molecules and select structures that can be prepared on automated systems and made available for biological testing, allowing for timely hypothesis testing and follow-up. Since its introduction, I2D has positively impacted several portfolio efforts through identification of new chemical scaffolds and functionalization of existing scaffolds. In this Innovations paper, we describe the I2D process and present an application for the discovery of new ULK inhibitors.

QSAR Model of Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain: Incorporating P-glycoprotein Efflux as a Variable

We report development and prospective validation of a QSAR model of the unbound brain-to-plasma partition coefficient, K_p,uu,brain, based on the in-house data set of ∼1000 compounds. We discuss effects of experimental variability, explore the applicability of both regression and classification approaches, and evaluate a novel, model-within-a-model approach of including P-glycoprotein efflux prediction as an additional variable. When tested on an independent test set of 91 internal compounds, incorporation of P-glycoprotein efflux information significantly improves the model performance resulting in an R² of 0.53, RMSE of 0.57, Spearman's Rho correlation coefficient of 0.73, and qualitative prediction accuracy of 0.8 (kappa = 0.6). In addition to improving the performance, one of the key advantages of this approach is the larger chemical space coverage provided indirectly through incorporation of the in vitro, higher throughput data set that is 4 times larger than the in vivo data set.

An overview of molecular fingerprint similarity search in virtual screening

INTRODUCTION

A central premise of medicinal chemistry is that structurally similar molecules exhibit similar biological activities. Molecular fingerprints encode properties of small molecules and assess their similarities computationally through bit string comparisons. Based on the similarity to a biologically active template, molecular fingerprint methods allow for identifying additional compounds with a higher chance of displaying similar biological activities against the same target - a process commonly referred to as virtual screening (VS).

AREAS COVERED

This article focuses on fingerprint similarity searches in the context of compound selection for enhancing hit sets, comparing compound decks, and VS. In addition, the authors discuss the application of fingerprints in predictive modeling.

EXPERT OPINION

Fingerprint similarity search methods are especially useful in VS if only a few unrelated ligands are known for a given target and therefore more complex and information rich methods such as pharmacophore searches or structure-based design are not applicable. In addition, fingerprint methods are used in characterizing properties of compound collections such as chemical diversity, density in chemical space, and content of biologically active molecules (biodiversity). Such assessments are important for deciding what compounds to experimentally screen, to purchase, or to assemble in a virtual compound deck for in silico screening or de novo design.

Fragment-based design of kinase inhibitors: a practical guide

Fragment-based drug design has become an important strategy for drug design and development over the last decade. It has been used with particular success in the development of kinase inhibitors, which are one of the most widely explored classes of drug targets today. The application of fragment-based methods to discovering and optimizing kinase inhibitors can be a complicated and daunting task; however, a general process has emerged that has been highly fruitful. Here a practical outline of the fragment process used in kinase inhibitor design and development is laid out with specific examples. A guide to the overall process from initial discovery through fragment screening, including the difficulties in detection, to the computational methods available for use in optimization of the discovered fragments is reported.

A high-throughput screen against pantothenate synthetase (PanC) identifies 3-biphenyl-4-cyanopyrrole-2-carboxylic acids as a new class of inhibitor with activity against Mycobacterium tuberculosis

The enzyme pantothenate synthetase, PanC, is an attractive drug target in Mycobacterium tuberculosis. It is essential for the in vitro growth of M. tuberculosis and for survival of the bacteria in the mouse model of infection. PanC is absent from mammals. We developed an enzyme-based assay to identify inhibitors of PanC, optimized it for high-throughput screening, and tested a large and diverse library of compounds for activity. Two compounds belonging to the same chemical class of 3-biphenyl-4- cyanopyrrole-2-carboxylic acids had activity against the purified recombinant protein, and also inhibited growth of live M. tuberculosis in manner consistent with PanC inhibition. Thus we have identified a new class of PanC inhibitors with whole cell activity that can be further developed.

De novo design of protein kinase inhibitors by in silico identification of hinge region-binding fragments

Protein kinases constitute an attractive family of enzyme targets with high relevance to cell and disease biology. Small molecule inhibitors are powerful tools to dissect and elucidate the function of kinases in chemical biology research and to serve as potential starting points for drug discovery. However, the discovery and development of novel inhibitors remains challenging. Here, we describe a structure-based de novo design approach that generates novel, hinge-binding fragments that are synthetically feasible and can be elaborated to small molecule libraries. Starting from commercially available compounds, core fragments were extracted, filtered for pharmacophoric properties compatible with hinge-region binding, and docked into a panel of protein kinases. Fragments with a high consensus score were subsequently short-listed for synthesis. Application of this strategy led to a number of core fragments with no previously reported activity against kinases. Small libraries around the core fragments were synthesized, and representative compounds were tested against a large panel of protein kinases and subjected to co-crystallization experiments. Each of the tested compounds was active against at least one kinase, but not all kinases in the panel were inhibited. A number of compounds showed high ligand efficiencies for therapeutically relevant kinases; among them were MAPKAP-K3, SRPK1, SGK1, TAK1, and GCK for which only few inhibitors are reported in the literature.

Applying ligands profiling using multiple extended electron distribution based field templates and feature trees similarity searching in the discovery of new generation of urea-based antineoplastic kinase inhibitors

This study provides a comprehensive computational procedure for the discovery of novel urea-based antineoplastic kinase inhibitors while focusing on diversification of both chemotype and selectivity pattern. It presents a systematic structural analysis of the different binding motifs of urea-based kinase inhibitors and the corresponding configurations of the kinase enzymes. The computational model depends on simultaneous application of two protocols. The first protocol applies multiple consecutive validated virtual screening filters including SMARTS, support vector-machine model (ROC = 0.98), Bayesian model (ROC = 0.86) and structure-based pharmacophore filters based on urea-based kinase inhibitors complexes retrieved from literature. This is followed by hits profiling against different extended electron distribution (XED) based field templates representing different kinase targets. The second protocol enables cancericidal activity verification by using the algorithm of feature trees (Ftrees) similarity searching against NCI database. Being a proof-of-concept study, this combined procedure was experimentally validated by its utilization in developing a novel series of urea-based derivatives of strong anticancer activity. This new series is based on 3-benzylbenzo[d]thiazol-2(3H)-one scaffold which has interesting chemical feasibility and wide diversification capability. Antineoplastic activity of this series was assayed in vitro against NCI 60 tumor-cell lines showing very strong inhibition of GI₅₀ as low as 0.9 uM. Additionally, its mechanism was unleashed using KINEX™ protein kinase microarray-based small molecule inhibitor profiling platform and cell cycle analysis showing a peculiar selectivity pattern against Zap70, c-src, Mink1, csk and MeKK2 kinases. Interestingly, it showed activity on syk kinase confirming the recent studies finding of the high activity of diphenyl urea containing compounds against this kinase. Allover, the new series, which is based on a new kinase scaffold with interesting chemical diversification capabilities, showed that it exhibits its “emergent” properties by perturbing multiple unexplored kinase pathways.

Novel kinase inhibitors by reshuffling ligand functionalities across the human kinome

Protein kinases remain among the most versatile and prospective therapeutic drug targets with currently 15 distinct compounds approved for use in humans and numerous clinical development programs. The vast majority of kinase inhibitors bind at the ATP site. Here we present an integrated workflow to amplify the rapidly increasing space of structurally resolved small molecule kinase ligands to generate novel inhibitors. Our approach considers both receptor-based similarity constraints in cocomplexes and ligand-based filtering/refinement methods to generate novel, drug-like matter. After building a comprehensive database of the structural kinome and identifying ATP-competitive ligands, we leverage local site similarities and site alignments to shuffle ligand fragments across the kinome. After extensive curation and standardization, our automated protocol starting from 936 cocrystal ATP-competitive binding sites generated about 150,000 new ligand structures among them over 26,000 lead-/drug-like compounds; the majority of those are novel based on structural similarity and scaffolds. In a retrospective analysis we demonstrate that our protocol produced known potent kinase inhibitors and we show how docking can be applied to prioritize the most likely efficacious compounds. Our workflow emulates a common strategy in medicinal chemistry to identify and swap corresponding moieties from known inhibitors to generate novel and potent leads. Here, we systematize and automate this approach leveraging available knowledge covering the entire human Kinome.

Investigation of the differences in activity between hydroxycycloalkyl N1 substituted pyrazole derivatives as inhibitors of B-Raf kinase by using docking, molecular dynamics, QM/MM, and fragment-based de novo design: study of binding mode of diastereomer compounds

N1 substituted pyrazole derivatives show diverse B-Raf kinase inhibitory activities when different hydroxy-substituted cycloalkyl groups are placed at this position. Docking, molecular dynamics (MD) simulations, and hybrid calculation methods (Quantum Mechanics/Molecular Mechanics (QM/MM)) were performed on the complexes, in order to explain these differences. Docking of the inhibitors showed the same orientation that X-ray crystal structure of the analogous (1E)-5-[1-(4-piperidinyl)-3-(4-pyridinyl)-1H-pyrazol-4-yl]-2,3-dihydro-1H-inden-1-one oxime. MD simulations of the most active diastereomer compounds containing cis- and trans-3-hydroxycyclohexyl substituents showed stable interactions with residue Ile463 at the entrance of the B-Raf active site. On the other hand, the less active diastereomer compounds containing cis- and trans-2-hydroxycyclopentyl substituents showed interactions with inner residues Asn580 and Ser465. We found that the differences in activity can be explained by considering the dynamic interactions between the inhibitors and their surrounding residues within the B-Raf binding site. We also explained the activity trend by using a testing scoring function derived from more reliable QM/MM calculations. In addition, we search for new inhibitors from a virtual screening carried out by fragment-based de novo design. We generated a set of approximately 200 virtual compounds, which interact with Ile463 and fulfill druglikeness properties according to Lipinski, Veber, and Ghose rules.

Study of differences in the VEGFR2 inhibitory activities between semaxanib and SU5205 using 3D-QSAR, docking, and molecular dynamics simulations

Semaxanib (SU5416) and 3-[4'-fluorobenzylidene]indolin-2-one (SU5205) are structurally similar drugs that are able to inhibit vascular endothelial growth factor receptor-2 (VEGFR2), but the former is 87 times more effective than the latter. Previously, SU5205 was used as a radiolabelled inhibitor (as surrogate for SU5416) and a radiotracer for positron emission tomography (PET) imaging, but the compound exhibited poor stability and only a moderate IC(50) toward VEGFR2. In the current work, the relationship between the structure and activity of these drugs as VEGFR2 inhibitors was studied using 3D-QSAR, docking and molecular dynamics (MD) simulations. First, comparative molecular field analysis (CoMFA) was performed using 48 2-indolinone derivatives and their VEGFR2 inhibitory activities. The best CoMFA model was carried out over a training set including 40 compounds, and it included steric and electrostatic fields. In addition, this model gave satisfactory cross-validation results and adequately predicted 8 compounds contained in the test set. The plots of the CoMFA fields could explain the structural differences between semaxanib and SU5205. Docking and molecular dynamics simulations showed that both molecules have the same orientation and dynamics inside the VEGFR2 active site. However, the hydrophobic pocket of VEGFR2 was more exposed to the solvent media when it was complexed with SU5205. An energetic analysis, including Embrace and MM-GBSA calculations, revealed that the potency of ligand binding is governed by van der Waals contacts.

The role of p27(Kip1) in dasatinib-enhanced paclitaxel cytotoxicity in human ovarian cancer cells

BACKGROUND

Less than 50% of ovarian cancers respond to paclitaxel. Effective strategies are needed to enhance paclitaxel sensitivity.

METHODS

A library of silencing RNAs (siRNAs) was used to identify kinases that regulate paclitaxel sensitivity in human ovarian cancer SKOv3 cells. The effect of dasatinib, an inhibitor of Src and Abl kinases, on paclitaxel sensitivity was measured in ovarian cancer cells and HEY xenografts. The roles of p27(Kip1), Bcl-2, and Cdk1 in apoptosis induced by dasatinib and paclitaxel were assessed using a terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay, siRNA knockdown of gene expression, transfection with Bcl-2 and Cdk1 expression vectors, and flow cytometry. All statistical tests were two-sided.

RESULTS

Src family and Abl kinases were identified as modulators of paclitaxel sensitivity in SKOv3 cells. The siRNA knockdown of Src, Fyn, or Abl1 enhanced paclitaxel-mediated growth inhibition in ovarian cancer cells compared with a control siRNA. HEY cells treated with dasatinib plus paclitaxel formed fewer colonies than did cells treated with either agent alone. Treatment of HEY xenograft-bearing mice with dasatinib plus paclitaxel inhibited tumor growth more than treatment with either agent alone (average tumor volume per mouse, dasatinib + paclitaxel vs paclitaxel: 0.28 vs. 0.81 cm3, difference = 0.53 cm3, 95% confidence interval [CI] = 0.44 to 0.62 cm3, P = .014); dasatinib + paclitaxel vs. dasatinib: 0.28 vs. 0.55 cm3, difference = 0.27 cm3, 95% CI = 0.21 to 0.33 cm3, P = .035). Combined treatment induced more TUNEL-positive apoptotic cells than did either agent alone. The siRNA knockdown of p27(Kip1) decreased dasatinib- and paclitaxel-induced apoptosis compared with a negative control siRNA (sub-G1 fraction, control siRNA vs. p27(Kip1) siRNA: 42.5% vs. 20.1%, difference = 22.4%, 95% CI = 20.1% to 24.7%, P = .017). Studies with forced expression and siRNA knockdown of Bcl-2 and Cdk1 suggest that dasatinib-mediated induction of p27(Kip1) enhanced paclitaxel-induced apoptosis by negatively regulating Bcl-2 and Cdk1 expression.

CONCLUSION

Inhibition of Src family and Abl kinases with either siRNAs or dasatinib enhances paclitaxel sensitivity of ovarian cancer cells through p27(Kip1)-mediated suppression of Bcl-2 and Cdk1 expression.

Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity

Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be active at 10 μM. These overall results are particularly striking as chemical novelty was an important criterion in selecting compounds for testing. The method is completely automated. Predicted activities for nearly 4 million internal and commercial compounds across 115 kinase assays and 42 cellular assays are stored in the corporate database. Like computed physical properties, this predicted kinase activity profile can be computed and stored as each compound is registered.

Docking and quantitative structure-activity relationship studies for 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline, and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors

We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC(50) values were obtained by using quantitative structure-activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.

Fragment-based approaches and computer-aided drug discovery

Fragment-based design has significantly modified drug discovery strategies and paradigms in the last decade. Besides technological advances and novel therapeutic avenues, one of the most significant changes brought by this new discipline has occurred in the minds of drug designers. Fragment-based approaches have markedly impacted rational computer-aided design both in method development and in applications. The present review illustrates the importance of molecular fragments in many aspects of rational ligand design, and discusses how thinking in "fragment space" has boosted computational biology and chemistry.

Bayesian methods in virtual screening and chemical biology

The Naïve Bayesian Classifier, as well as related classification and regression approaches based on Bayes' theorem, has experienced increased attention in the cheminformatics world in recent years. In this contribution, we first review the mathematical framework on which Bayes' methods are built, and then continue to discuss implications of this framework as well as practical experience under which conditions Bayes' methods give the best performance in virtual screening settings. Finally, we present an overview of applications of Bayes' methods to both virtual screening and the chemical biology arena, where applications range from bridging phenotypic and mechanistic space of drug action to the prediction of ligand-target interactions.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

Computational Modeling of Kinase Inhibitor Selectivity

An exhaustive computational exercise on a comprehensive set of 15 therapeutic kinase inhibitors was undertaken to identify as to which compounds hit which kinase off-targets in the human kinome. Although the kinase selectivity propensity of each inhibitor against ∼480 kinase targets is predicted, we compared our predictions to ∼280 kinase targets for which consistent experimental data are available and demonstrate an overall average prediction accuracy and specificity of ∼90%. A comparison of the predictions was extended to an additional ∼60 kinases for sorafenib and sunitinib as new experimental data were reported recently with similar prediction accuracy. The successful predictive capabilities allowed us to propose predictions on the remaining kinome targets in an effort to repurpose known kinase inhibitors to these new kinase targets that could hold therapeutic potential.

Computational study on the interaction of N1 substituted pyrazole derivatives with B-raf kinase: an unusual water wire hydrogen-bond network and novel interactions at the entrance of the active site

Docking and molecular dynamics (MD) simulations of N1 substituted pyrazole derivatives complexed with B-Raf kinase were performed to gain insight into the structural and energetic preferences of these inhibitors. First, a comparative study of fully automated docking programs AutoDock, ICM, GLIDE, and Surflex-Dock in closely approximating the X-ray crystal structure of the inhibitor (1E)-5-[1-(4-piperidinyl)-3-(4-pyridinyl)-1H-pyrazol-4-yl]-2,3-dihydro-1H-inden-1-one oxime was performed. Afterward, the dynamics of the above-mentioned compound and the less active analogous compounds with 1-methyl-4-piperidinyl and tetrahydro-2H-pyran-4-yl groups at position N1 of pyrazole ring inside the B-Raf active site were analyzed by MD simulations. We found that the most active compound has stable interactions with residues Ile463 and His539 at the entrance of the B-Raf active site. Those interactions were in very good agreement with more reliable quantum mechanics/molecular mechanics calculations performed on the torsional angle phi between the pyrazole ring and the substituents at position N1. In addition, we identified a water wire connecting N2 of the pyrazole ring, Cys532, and Ser536, which is composed of three water molecules for the most active compound. We found some differences in the water wire hydrogen-bond network formed by less active compounds. We suggest that the differences between these structural features are responsible for the differences in activity among the studied compounds.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Enhanced interrogation: emerging strategies for cell signaling inhibition

Here we summarize recent and developing chemical approaches for modulating signaling pathways. In particular, we discuss targeting mutant signaling proteins, disrupting protein-protein interactions in cellular signaling networks, designing bivalent inhibitors of signaling proteins and identifying allosteric regulators of signaling enzymes. Over the past decade, great progress in the harvesting of chemical tools for basic research and clinical medicine has been made, but many challenges remain, and examples of exciting future targets are highlighted.

Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs

The similarity ensemble approach (SEA) relates proteins based on the set-wise chemical similarity among their ligands. It can be used to rapidly search large compound databases and to build cross-target similarity maps. The emerging maps relate targets in ways that reveal relationships one might not recognize based on sequence or structural similarities alone. SEA has previously revealed cross talk between drugs acting primarily on G-protein coupled receptors (GPCRs). Here we used SEA to look for potential off-target inhibition of the enzyme protein farnesyltransferase (PFTase) by commercially available drugs. The inhibition of PFTase has profound consequences for oncogenesis, as well as a number of other diseases. In the present study, two commercial drugs, Loratadine and Miconazole, were identified as potential ligands for PFTase and subsequently confirmed as such experimentally. These results point toward the applicability of SEA for the prediction of not only GPCR-GPCR drug cross talk but also GPCR-enzyme and enzyme-enzyme drug cross talk.

When analoging is not enough: scaffold discovery in medicinal chemistry

IMPORTANCE OF THE FIELD

As an integral part of lead generation and optimization, scaffold discovery has broad implications in drug discovery. Currently available chemical scaffolds might be inadequate to provide drug-like ligands for new targets such as phosphatases and protein-protein interactions and therapeutically useful chemical space needs to be continuously explored. New scaffolds are often desired to overcome major hurdles (e.g., potency plateau, selectivity, pharmacokinetics, etc.) in lead generation and optimization. Timely discovery of proof-of-concept compounds facilitates target validation, diversifies clinical candidates and improves the overall success rate of drug discovery.

AREAS COVERED IN THIS REVIEW

This analysis discusses the strategies involved in finding new scaffolds (i.e., fragment-, ligand- and structure-based design) and their applications (e.g., improve potency/selectivity, multiple ligand design, protein-protein interactions, etc.) in drug discovery.

WHAT THE READER WILL GAIN

The readers will learn the strategies involved in scaffold design and the problems that they solve. They will also gain the understanding of the circumstances suitable for using scaffold design.

TAKE HOME MESSAGE

Scaffold is defined by the authors as a biological target dependent concept. Therapeutically useful scaffolds are limited and the identification of new scaffolds is sometimes required to overcome major optimization hurdles. However, depending on the promiscuity of the binding pocket of the target and the validity of the optimization protocol, finding better scaffolds can be a challenging task. Several strategies in scaffold discovery have emerged or matured owing to recent trends such as pursuit of targets from new proteomic families, lack of validated targets, advances in synthesis and biological assays and adoption of in vitro activity-driven screening paradigms.