Discovery of novel fibroblast growth factor receptor 1 kinase inhibitors by structure-based virtual screening

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Absolute binding free energy calculations improve enrichment of actives in virtual compound screening

We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Investigation on penetration of saffron components through lipid bilayer bound to spike protein of SARS-CoV-2 using steered molecular dynamics simulation

A coronavirus identified as COVID-19 is the reason for an infection outbreak which is started in December 2019. NO completely effective drugs and treatments are not recognized for this virus. Recently, saffron and its compounds were used to treat different viral diseases. Saffron extract and its major ingredients have shown antiviral effects. In this study, the steered molecular dynamics simulation was used for investigating the effect of four main components of saffron that include: crocin, crocetin, safranal, and picrocrocin as candidate for drug molecules, on COVID-19. The binding energies between drug molecules and spike protein and the main protease of the virus were evaluated. The obtained results based on Lennard-Jones and electrostatic potentials demonstrated that crocetin has a high affinity towards spike protein and also the main protease of the virus. Also, the quantum mechanics calculations elucidated that the crocetin could overcome energy barrier of lipid bilayer with strong dipole moment and polarizability. The pharmacokinetic and ADMET properties proved that crocetin could be a suitable drug candidate. So, crocetin could be a promising drug for treatment of COVID-19.

Synthesis and characterization of 1,2,4-triazolo[1,5-a]pyrimidine-2-carboxamide-based compounds targeting the PA-PB1 interface of influenza A virus polymerase

Influenza viruses (Flu) are responsible for seasonal epidemics causing high rates of morbidity, which can dramatically increase during severe pandemic outbreaks. Antiviral drugs are an indispensable weapon to treat infected people and reduce the impact on human health, nevertheless anti-Flu armamentarium still remains inadequate. In search for new anti-Flu drugs, our group has focused on viral RNA-dependent RNA polymerase (RdRP) developing disruptors of PA-PB1 subunits interface with the best compounds characterized by cycloheptathiophene-3-carboxamide and 1,2,4-triazolo[1,5-a]pyrimidine-2-carboxamide scaffolds. By merging these moieties, two very interesting hybrid compounds were recently identified, starting from which, in this paper, a series of analogues were designed and synthesized. In particular, a thorough exploration of the cycloheptathiophene-3-carboxamide moiety led to acquire important SAR insight and identify new active compounds showing both the ability to inhibit PA-PB1 interaction and viral replication in the micromolar range and at non-toxic concentrations. For few compounds, the ability to efficiently inhibit PA-PB1 subunits interaction did not translate into anti-Flu activity. Chemical/physical properties were investigated for a couple of compounds suggesting that the low solubility of compound 14, due to a strong crystal lattice, may have impaired its antiviral activity. Finally, computational studies performed on compound 23, in which the phenyl ring suitably replaced the cycloheptathiophene, suggested that, in addition to hydrophobic interactions, H-bonds enhanced its binding within the PA_C cavity.

Rigorous Free Energy Simulations in Virtual Screening

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

Exploring structural requirements of isoform selective histone deacetylase inhibitors: a comparative in silico study

Histone deacetylases (HDACs) are a widely popular class of epigenetic regulators, second only in importance to DNA methyltransferases. They are responsible for deacetylating the lysine residues of a wide range of proteins, both nuclear and cytoplasmic. Therefore, deregulated HDAC activity is implicated in disruption of important biological functions leading to cancerous, neuropathological, infectious and inflammatory diseased states. The current therapeutic strategies aimed at combating HDAC related pathologies consist of pan HDAC inhibitors that target multiple HDAC isoforms. Many side-effects of such therapeutics have been reported due to off-target effects. Hence, efforts need to be focused towards developing therapeutics targeting single isoforms. This work aims at recognizing structural features, both of receptors and inhibitors, that would help achieve selective inhibition of HDAC isoforms. Protein alignment studies have been carried out to define the receptor structure differences that can be exploited for this purpose. Binding modes of highly isoform selective inhibitors have been established through molecular docking studies to characterize the receptor-ligand interactions responsible for selective inhibition. This information is represented with the help of pharmacophore models.

New fluorescence-based high-throughput screening assay for small molecule inhibitors of tyrosyl-DNA phosphodiesterase 2 (TDP2)

Tyrosyl-DNA phosphodiesterase 2 (TDP2) repairs topoisomerase II (TOP2) mediated DNA damages and causes resistance to TOP2-targeted cancer therapy. Inhibiting TDP2 could sensitize cancer cells toward TOP2 inhibitors. However, potent TDP2 inhibitors with favorable physicochemical properties are not yet reported. Therefore, there is a need to search for novel molecular scaffolds capable of inhibiting TDP2. We report herein a new simple, robust, homogenous mix-and-read fluorescence biochemical assay based using humanized zebrafish TDP2 (14M_zTDP2), which provides biochemical and molecular structure basis for TDP2 inhibitor discovery. The assay was validated by screening a preselected library of 1600 compounds (Z' ≥ 0.72) in a 384-well format, and by running in parallel gel-based assays with fluorescent DNA substrates. This library was curated via virtual high throughput screening (vHTS) of 460,000 compounds from Chembridge Library, using the crystal structure of the novel surrogate protein 14M_zTDP2. From this primary screening, we selected the best 32 compounds (2% of the library) to further assess their TDP2 inhibition potential, leading to the IC₅₀ determination of 10 compounds. Based on the dose-response curve profile, pan-assay interference compounds (PAINS) structure identification, physicochemical properties and efficiency parameters, two hit compounds, 11a and 19a, were tested using a novel secondary fluorescence gel-based assay. Preliminary structure-activity relationship (SAR) studies identified guanidine derivative 12a as an improved hit with a 6.4-fold increase in potency over the original HTS hit 11a. This study highlights the importance of the development of combination approaches (biochemistry, crystallography and high throughput screening) for the discovery of TDP2 inhibitors.

Design, synthesis and anticancer studies of novel aminobenzazolyl pyrimidines as tyrosine kinase inhibitors

Abnormal signalling from the Protein tyrosine kinases (PTKs) like receptor tyrosine kinases and intracellular tyrosine kinases can lead to diseases such as cancer especially non-small cell lung cancer, chronic myeloid leukaemia and gastrointestinal stromal tumours. Various Protein tyrosine kinase inhibitors are available but face poor bioavailability, severe toxicities and recent cases of drug-resistant cancers prompts for development of better drug molecules. In this study we report the design and development of a novel Protein Tyrosine Kinase (PTK) inhibitor on the basis of pharmacophore modelling. Compound 2-(benzo[d]oxazol-2-ylamino)-N-(2-chloro-4-fluorophenyl)-4-methyl-6-(3-nitrophenyl) pyrimidine-5-carboxamide 31 was obtained containing essential pharmacophore structural features. This compound exhibited highest activity against leukaemia cell line (RPMI-8226) at 0.7244 µM, renal cancer cell line (A498) at 0.8511 µM and prostate cancer cell line (PC-3) at 0.7932 µM on the NCI five dose assay test. The PTK assay provides promising activity at IC₅₀ of 0.07 µM in the human breast cancer cell line MDA-MB-468. Compound 31 had good intermolecular interaction with PTK in the molecular docking studies, this ligand-enzyme complex was found to stable in the MM-PBSA study over 100 ns. It had 54.22% oral bioavailability with T_max of 0.60 h which is higher compared to the dasatinib with bioavailability and T_max of 14-34% and 1-1.42 h respectively. Anticancer action of 31 was found to be impressive in pharmacokinetic studies making it a potential lead molecule.

Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation

AIM

FGFR-1 is an oncogenic kinase involved in several cancers. FGFR1-specific inhibitors have shown promising results against several human cancers prompting us to model this interesting target. Toward the end, we implemented elaborate ligand-based and structure-based computational workflows to explore the pharmacophoric requirements for potent FGFR-1 inhibitors. Results & methodology: Structure-based and ligand-based modeling applied on 59 diverse FGFR-1 inhibitors yielded novel pharmacophore and quantitative structure-activity relationship models that were used to scan the National Cancer Institute's structural database for novel leads. Four potent hits were captured, with the most active having IC₅₀ of 426 nM. Identities and purities of active hits were established using nuclear magnetic resonance and mass spectroscopy.

CONCLUSION

Elaborate ligand-based (pharmacophore/quantitaive structure-activity relationship) and structure-based (docking-based comparative intermolecular contacts analysis) modeling provided deep understanding of ligand binding within FGFR-1 as evidenced by the virtually captured new potent leads.

Design, synthesis, and biological evaluation of 3-vinyl-quinoxalin-2(1H)-one derivatives as novel antitumor inhibitors of FGFR1

FGFR1 is well known as a molecular target in anticancer drug design. TKI258 plays an important role in RTK inhibitors. Utilizing TKI258 as a lead compound that contains a quinazolinone nucleus, we synthesized four series of 3-vinyl-quinoxalin-2(1H)-one derivatives, a total of 27 compounds. We further evaluated these compounds for FGFR1 inhibition ability as well as cytotoxicity against four cancer cell lines (H460, B16-F10, Hela229, and Hct116) in vitro. Some compounds displayed good-to-excellent potency against the four tested cancer cell lines compared with TKI258. Structure–activity relationship analyses indicated that small substituents at the side chain of the 3-vinyl-quinoxalin-2(1H)-one were more effective than large substituents. Lastly, we used molecular docking to obtain further insight into the interactions between the compounds and FGFR1.

Synthesis and Evaluation of Biological and Antitumor Activities of Tetrahydrobenzothieno[2,3-d]Pyrimidine Derivatives as Novel Inhibitors of FGFR1

A series of tetrahydrobenzothieno[2,3-d]pyrimidine derivatives were designed, synthesized, and evaluated as inhibitors of FGFR1. These analogs were synthesized via Gewald's reaction under mild conditions. The structures of the synthesized compounds were characterized by spectroscopic data (IR, (1) H NMR and MS). Their antitumor activities were evaluated against H460, A549 and U251 cell lines in vitro. Results revealed that the tested compounds showed moderate antitumor activities. Structure-activity relationship analyses indicated that compounds with an aromatic ring substituted in the C-2 position or with larger molecules such as 3g, 4c, and 7 were more effective than others. The compound, 3g (78.8% FGFR1 inhibition at 10 μm), was identified to have the most potent antitumor activities, with IC50 values of 7.7, 18.9, and 13.3 μm against the H460, A549, and U251 cell lines, respectively. Together, the results suggested that tetrahydrobenzothieno[2,3-d]pyrimidine derivatives may serve as a potential agent for the treatment of FGFR1-mediated cancers.

In-silico identification of inhibitors against mutated BCR-ABL protein of chronic myeloid leukemia: a virtual screening and molecular dynamics simulation study

Aberrant and proliferative expression of the oncogene BCR-ABL in the bone marrow cells had been proven as the prime cause of chronic myeloid leukemia (CML). It has been established that tyrosine kinase domain of BCR-ABL protein is a potential therapeutic target for the treatment of CML. Imatinib is considered as a first-generation drug that can inhibit the enzymatic action by inhibiting the ATP binding with BCR-ABL protein. Later on, insensitivity of CML cells towards Imatinib has been observed may be due to mutation in tyrosine kinase domain of the ABL receptor. Subsequently, some other second-generation drugs have also been reported viz. Baustinib, Nilotinib, Dasatinib, Ponatinib, Bafetinib, etc., which can able to combat against mutated domain of ABL tyrosine kinase protein. By taking into account of bioavailability and resistance developed, there is an utmost need to find some more inhibitors for the mutated ABL tyrosine kinase protein. For virtual screening, a data-set has been generated by collecting the all available drug like natural compounds from ZINC and Drug Bank databases. Comparative docking analysis was also carried out on the active site of ABL tyrosine kinase receptor with reported reference inhibitors. Molecular dynamics simulation of the best screened interacting complex was done for 50 ns to validate the stability of the system. These selected inhibitors were further validated and analyzed through pharmacokinetics properties and series of ADMET parameters by in silico methods. Considering the above said parameters proposed molecules are concluded as potential leads for drug designing pipeline against CML.

Illuminating the molecular mechanisms of tyrosine kinase inhibitor resistance for the FGFR1 gatekeeper mutation: the Achilles' heel of targeted therapy

Human fibroblast growth factor receptors (FGFRs) 1-4 are a family of receptor tyrosine kinases that can serve as drivers of tumorigenesis. In particular, FGFR1 gene amplification has been implicated in squamous cell lung and breast cancers. Tyrosine kinase inhibitors (TKIs) targeting FGFR1, including AZD4547 and E3810 (Lucitanib), are currently in early phase clinical trials. Unfortunately, drug resistance limits the long-term success of TKIs, with mutations at the "gatekeeper" residue leading to tumor progression. Here we show the first structural and kinetic characterization of the FGFR1 gatekeeper mutation, V561M FGFR1. The V561M mutation confers a 38-fold increase in autophosphorylation achieved at least in part by a network of interacting residues forming a hydrophobic spine to stabilize the active conformation. Moreover, kinetic assays established that the V561M mutation confers significant resistance to E3810, while retaining affinity for AZD4547. Structural analyses of these TKIs with wild type (WT) and gatekeeper mutant forms of FGFR1 offer clues to developing inhibitors that maintain potency against gatekeeper mutations. We show that AZD4547 affinity is preserved by V561M FGFR1 due to a flexible linker that allows multiple inhibitor binding modes. This is the first example of a TKI binding in distinct conformations to WT and gatekeeper mutant forms of FGFR, highlighting adaptable regions in both the inhibitor and binding pocket crucial for drug design. Exploiting inhibitor flexibility to overcome drug resistance has been a successful strategy for combatting diseases such as AIDS and may be an important approach for designing inhibitors effective against kinase gatekeeper mutations.

Using docking and alchemical free energy approach to determine the binding mechanism of eEF2K inhibitors and prioritizing the compound synthesis

A-484954 is a known eEF2K inhibitor with submicromolar IC₅₀ potency. However, the binding mechanism and the crystal structure of the kinase remains unknown. Here, we employ a homology eEF2K model, docking and alchemical free energy simulations to probe the binding mechanism of eEF2K, and in turn, guide the optimization of potential lead compounds. The inhibitor was docked into the ATP-binding site of a homology model first. Three different binding poses, hypothesis 1, 2, and 3, were obtained and subsequently applied to molecular dynamics (MD) based alchemical free energy simulations. The calculated relative binding free energy of the analogs of A-484954 using the binding pose of hypothesis 1 show a good correlation with the experimental IC₅₀ values, yielding an r² coefficient of 0.96 after removing an outlier (compound 5). Calculations using another two poses show little correlation with experimental data, (r² of less than 0.5 with or without removing any outliers). Based on hypothesis 1, the calculated relative free energy suggests that bigger cyclic groups, at R1 e.g., cyclobutyl and cyclopentyl promote more favorable binding than smaller groups, such as cyclopropyl and hydrogen. Moreover, this study also demonstrates the ability of the alchemical free energy approach in combination with docking and homology modeling to prioritize compound synthesis. This can be an effective means of facilitating structure-based drug design when crystal structures are not available.

Synthesis and evaluation of biological and antitumor activities of 5,7-dimethyl- oxazolo[5,4-d]pyrimidine-4,6(5H,7H)-dione derivatives as novel inhibitors of FGFR1

A series of 5,7-dimethyl-oxazolo[5,4-d]pyrimidine-4,6(5H,7H)-dione derivatives, N5a-5l, was designed, synthesized and evaluated for their FGFR1-inhibition ability as well as cytotoxicity against three cancer cell lines (H460, B16F10 and A549) in vitro. Several compounds displayed good-to-excellent potency against these cancer cell lines compared to SU5402. Structure-activity relationship analyses indicated that compounds with a rigid structure and more heteroatoms at the side chain of the parent ring were more effective than those without these substitutions. The compound N5g (37.4% FGFR1 inhibition at 1.0 μM) was identified to have the most potent antitumor activities, with IC50 values of 5.472, 4.260 and 5.837 μM against H460, B16F10 and A549 cell lines, respectively. Together, our results suggest that 5,7-dimethyl-oxazolo[5,4-d]pyrimidine-4,6(5H,7H)-dione derivatives may serve as potential agents for the treatment of FGFR1-mediated cancers.

Synthesis and biological evaluation of novel indole-2-one and 7-aza-2-oxindole derivatives as anti-inflammatory agents

Sepsis, a typically acute inflammatory disease, is the biggest cause of death in ICU (intensive care unit). Novel anti-inflammatory alternatives are still in urgent need. In this study, we designed and synthesized 30 indole-2-one and 7-aza-2-oxindole derivatives based on the skeleton of tenidap, and their anti-inflammatory activity was determined by evaluating the inhibitory potency against lipopolysaccharide (LPS)-stimulated tumor necrosis factor (TNF)-α and interleukin (IL)-6 release in RAW264.7 macrophages. Quantitative SAR (structure-activity relationship) analysis revealed that a high molecular polarizability and low lipid/water partition coefficient (ALogP) in indole-2-one are beneficial for anti-inflammatory activity. Moreover, compounds 7i and 8e inhibited the expression of TNF-α, IL-6, COX-2, PGES, and iNOS in LPS-stimulated macrophages, and 7i exhibited a significant protection from LPS-induced septic death in mouse models. These data present a series of new indole-2-one compounds with potential therapeutic effects in acute inflammatory diseases.

Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand–receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein–ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets.

Protein tyrosine phosphatases: Ligand interaction analysis and optimisation of virtual screening

Docking-based virtual screening is an established component of structure-based drug discovery. Nevertheless, scoring and ranking of computationally docked ligand libraries still suffer from many false positives. Identifying optimal docking parameters for a target protein prior to virtual screening can improve experimental hit rates. Here, we examine protocols for virtual screening against the important but challenging class of drug target, protein tyrosine phosphatases. In this study, common interaction features were identified from analysis of protein-ligand binding geometries of more than 50 complexed phosphatase crystal structures. It was found that two interactions were consistently formed across all phosphatase inhibitors: (1) a polar contact with the conserved arginine residue, and (2) at least one interaction with the P-loop backbone amide. In order to investigate the significance of these features on phosphatase-ligand binding, a series of seeded virtual screening experiments were conducted on three phosphatase enzymes, PTP1B, Cdc25b and IF2. It was observed that when the conserved arginine and P-loop amide interactions were used as pharmacophoric constraints during docking, enrichment of the virtual screen significantly increased in the three studied phosphatases, by up to a factor of two in some cases. Additionally, the use of such pharmacophoric constraints considerably improved the ability of docking to predict the inhibitor's bound pose, decreasing RMSD to the crystallographic geometry by 43% on average. Constrained docking improved enrichment of screens against both open and closed conformations of PTP1B. Incorporation of an ordered water molecule in PTP1B screening was also found to generally improve enrichment. The knowledge-based computational strategies explored here can potentially inform structure-based design of new phosphatase inhibitors using docking-based virtual screening.

Identification and validation of novel PERK inhibitors

PERK, as one of the principle unfolded protein response signal transducers, is believed to be associated with many human diseases, such as cancer and type-II diabetes. There has been increasing effort to discover potent PERK inhibitors due to its potential therapeutic interest. In this study, a computer-based virtual screening approach is employed to discover novel PERK inhibitors, followed by experimental validation. Using a focused library, we show that a consensus approach, combining pharmacophore modeling and docking, can be more cost-effective than using either approach alone. It is also demonstrated that the conformational flexibility near the active site is an important consideration in structure-based docking and can be addressed by using molecular dynamics. The consensus approach has further been applied to screen the ZINC lead-like database, resulting in the identification of 10 active compounds, two of which show IC50 values that are less than 10 μM in a dose-response assay.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Ligand pose and orientational sampling in molecular docking

Molecular docking remains an important tool for structure-based screening to find new ligands and chemical probes. As docking ambitions grow to include new scoring function terms, and to address ever more targets, the reliability and extendability of the orientation sampling, and the throughput of the method, become pressing. Here we explore sampling techniques that eliminate stochastic behavior in DOCK3.6, allowing us to optimize the method for regularly variable sampling of orientations. This also enabled a focused effort to optimize the code for efficiency, with a three-fold increase in the speed of the program. This, in turn, facilitated extensive testing of the method on the 102 targets, 22,805 ligands and 1,411,214 decoys of the Directory of Useful Decoys - Enhanced (DUD-E) benchmarking set, at multiple levels of sampling. Encouragingly, we observe that as sampling increases from 50 to 500 to 2000 to 5000 to 20000 molecular orientations in the binding site (and so from about 1×10¹⁰ to 4×10¹⁰ to 1×10¹¹ to 2×10¹¹ to 5×10¹¹ mean atoms scored per target, since multiple conformations are sampled per orientation), the enrichment of ligands over decoys monotonically increases for most DUD-E targets. Meanwhile, including internal electrostatics in the evaluation ligand conformational energies, and restricting aromatic hydroxyls to low energy rotamers, further improved enrichment values. Several of the strategies used here to improve the efficiency of the code are broadly applicable in the field.

In silico identification of EGFR-T790M inhibitors with novel scaffolds: start with extraction of common features

Background

Epidermal growth factor receptor (EGFR) is an attractive therapeutic target for a number of human tumors including non-small cell lung cancer (NSCLC). Most patients with NSCLC and somatic mutations have shown a dramatic initial clinical response to reversible EGFR inhibitors. The clinical efficacy of reversible inhibitors is, however, ultimately limited due to the emergence of drug resistance, which is usually conferred by the EGFR T790M mutation. Importantly, irreversible, synthetic small molecule inhibitors are currently evaluated and some of them have been shown to overcome the acquired resistance that is oftentimes observed in these patients. Thus far, irreversible EGFR inhibitors as a drug class have not received regulatory approval due in part to their poor effectiveness at clinically achievable concentrations. Therefore, there is an urgent need to discover and develop novel, potent irreversible inhibitors against the EGFR T790M mutation.

Material and methods

In the following study, we report a novel “hybrid strategy” to identify irreversible EGFR inhibitors with active scaffolds starting with the identification and extraction of a common chemical reactive feature and a pharmacophore feature. The chemical reactive feature was elucidated by investigating 138 currently known irreversible inhibitors at B3LYP/6-31G(d) level using the density function theory method. The pharmacophore feature was extracted from the same inhibitors using pharmacophore modeling. Based on these unique features, two constraints were set while calibrating the protocols of in silico screening. Compounds bearing these specific features were obtained from the National Cancer Institute diversity database to form our subsequent library. Finally, a structure based virtual screening against the library was conducted using standard protocols validated in our lab.

Results

Twenty-eight candidate compounds that demonstrated antitumor activity and that had novel scaffolds different from commonly known quinazoline/quinoline analogs were obtained. The interaction modes between three representative candidates and our model system are similar to that between the model system and the reference compound T-001, which has previously been reported to be one of the most potent of the 138 irreversible inhibitors.

Conclusion

The hybrid strategy starting with the extraction of common features is an effective approach to design potential irreversible inhibitors with novel scaffolds and therefore to obtain lead molecules in the selection process. These candidates possessing unique scaffolds have a strong likelihood to act as further starting points in the preclinical development of potent irreversible T790M EGFR inhibitors.

Current strategies for inhibiting FGFR activities in clinical applications: opportunities, challenges and toxicological considerations

Aberrations in fibroblast growth factor receptor (FGFR) signaling are instrumental to the pathophysiology of several malignancies and disorders. Hence, FGFR inhibitors are explored in therapeutics with early candidates developed as competitors for the ATP-binding pocket in the kinase domain. More recent programs yielded compounds of diverse scaffolds with alternative binding modes. Concurrently, monoclonal antibodies and peptide-based agents provide independent options for clinical development. Notwithstanding this rapid progress, we contemplate the toxicological impact of FGFR inhibition based on the defined role of FGFR family members in physiology and homeostasis. The high homology among FGFR1-4 and also with other kinase subfamilies creates an additional challenge in developing selective inhibitors. It orchestrates an ongoing conundrum of moderating a balance between synergism through multitargeting kinase inhibition and minimizing off-target toxicities.

Parallel screening of wild-type and drug-resistant targets for anti-resistance neuraminidase inhibitors

Infection with influenza virus is a major public health problem, causing serious illness and death each year. Emergence of drug-resistant influenza virus strains limits the effectiveness of drug treatment. Importantly, a dual H275Y/I223R mutation detected in the pandemic influenza A 2009 virus strain results in multidrug resistance to current neuraminidase (NA) drugs. Therefore, discovery of new agents for treating multiple drug-resistant (MDR) influenza virus infections is important. Here, we propose a parallel screening strategy that simultaneously screens wild-type (WT) and MDR NAs, and identifies inhibitors matching the subsite characteristics of both NA-binding sites. These may maintain their potency when drug-resistant mutations arise. Initially, we analyzed the subsite of the dual H275Y/I223R NA mutant. Analysis of the site-moiety maps of NA protein structures show that the mutant subsite has a relatively small volume and is highly polar compared with the WT subsite. Moreover, the mutant subsite has a high preference for forming hydrogen-bonding interactions with polar moieties. These changes may drive multidrug resistance. Using this strategy, we identified a new inhibitor, Remazol Brilliant Blue R (RB19, an anthraquinone dye), which inhibited WT NA and MDR NA with IC₅₀ values of 3.4 and 4.5 µM, respectively. RB19 comprises a rigid core scaffold and a flexible chain with a large polar moiety. The former interacts with highly conserved residues, decreasing the probability of resistance. The latter forms van der Waals contacts with the WT subsite and yields hydrogen bonds with the mutant subsite by switching the orientation of its flexible side chain. Both scaffolds of RB19 are good starting points for lead optimization. The results reveal a parallel screening strategy for identifying resistance mechanisms and discovering anti-resistance neuraminidase inhibitors. We believe that this strategy may be applied to other diseases with high mutation rates, such as cancer and human immunodeficiency virus type 1.

Design, synthesis, and antiviral activity of entry inhibitors that target the CD4-binding site of HIV-1

The CD4 binding site on HIV-1 gp120 has been validated as a drug target to prevent HIV-1 entry to cells. Previously, we identified two small molecule inhibitors consisting of a 2,2,6,6-tetramethylpiperidine ring linked by an oxalamide to a p-halide-substituted phenyl group, which target this site, specifically, a cavity termed "Phe43 cavity". Here we use synthetic chemistry, functional assessment, and structure-based analysis to explore variants of each region of these inhibitors for improved antiviral properties. Alterations of the phenyl group and of the oxalamide linker indicated that these regions were close to optimal in the original lead compounds. Design of a series of compounds, where the tetramethylpiperidine ring was replaced with new scaffolds, led to improved antiviral activity. These new scaffolds provide insight into the surface chemistry at the entrance of the cavity and offer additional opportunities by which to optimize further these potential-next-generation therapeutics and microbicides against HIV-1.

Structure-based virtual screening for drug discovery: a problem-centric review

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.

A novel tyrosine kinase inhibitor restores chondrocyte differentiation and promotes bone growth in a gain-of-function Fgfr3 mouse model

Activating germline fibroblast growth factor receptor 3 (FGFR3) mutations cause achondroplasia (ACH), the most common form of human dwarfism and a spectrum of skeletal dysplasias. FGFR3 is a tyrosine kinase receptor and constitutive FGFR3 activation impairs endochondral ossification and triggers severe disorganization of the cartilage with shortening of long bones. To decipher the role of FGFR3 in endochondral ossification, we analyzed the impact of a novel tyrosine kinase inhibitor (TKI), A31, on both human and mouse mutant FGFR3-expressing cells and on the skeleton of Fgfr3(Y367C/+) dwarf mice. We found that A31 inhibited constitutive FGFR3 phosphorylation and restored the size of embryonic dwarf femurs using an ex vivo culture system. The increase in length of the treated mutant femurs was 2.6 times more than for the wild-type. Premature cell cycle exit and defective chondrocyte differentiation were observed in the Fgfr3(Y367C/+) growth plate. A31 restored normal expression of cell cycle regulators (proliferating cell nuclear antigen, KI67, cyclin D1 and p57) and allowed pre-hypertrophic chondrocytes to properly differentiate into hypertrophic chondocytes. Our data reveal a specific role for FGFR3 in the cell cycle and chondrocyte differentiation and support the development of TKIs for the treatment of FGFR3-related chondrodysplasias.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

Using free energy of binding calculations to improve the accuracy of virtual screening predictions

Virtual screening of small molecule databases against macromolecular targets was used to identify binding ligands and predict their lowest energy bound conformation (i.e., pose). AutoDock4-generated poses were rescored using mean-field pathway decoupling free energy of binding calculations and evaluated if these calculations improved virtual screening discrimination between bound and nonbound ligands. Two small molecule databases were used to evaluate the effectiveness of the rescoring algorithm in correctly identifying binders of L99A T4 lysozyme. Self-dock calculations of a database containing compounds with known binding free energies and cocrystal structures largely reproduced experimental measurements, although the mean difference between calculated and experimental binding free energies increased as the predicted bound poses diverged from the experimental poses. In addition, free energy rescoring was more accurate than AutoDock4 scores in discriminating between known binders and nonbinders, suggesting free energy rescoring could be a useful approach to reduce false positive predictions in virtual screening experiments.

Task-parallel message passing interface implementation of Autodock4 for docking of very large databases of compounds using high-performance super-computers

A message passing interface (MPI)-based implementation (Autodock4.lga.MPI) of the grid-based docking program Autodock4 has been developed to allow simultaneous and independent docking of multiple compounds on up to thousands of central processing units (CPUs) using the Lamarkian genetic algorithm. The MPI version reads a single binary file containing precalculated grids that represent the protein-ligand interactions, i.e., van der Waals, electrostatic, and desolvation potentials, and needs only two input parameter files for the entire docking run. In comparison, the serial version of Autodock4 reads ASCII grid files and requires one parameter file per compound. The modifications performed result in significantly reduced input/output activity compared with the serial version. Autodock4.lga.MPI scales up to 8192 CPUs with a maximal overhead of 16.3%, of which two thirds is due to input/output operations and one third originates from MPI operations. The optimal docking strategy, which minimizes docking CPU time without lowering the quality of the database enrichments, comprises the docking of ligands preordered from the most to the least flexible and the assignment of the number of energy evaluations as a function of the number of rotatable bounds. In 24 h, on 8192 high-performance computing CPUs, the present MPI version would allow docking to a rigid protein of about 300K small flexible compounds or 11 million rigid compounds.

Aryl Extensions of Thienopyrimidinones as Fibroblast Growth Factor Receptor 1 Kinase Inhibitors

Optimization of thienopyrimidinone derivatives as FGFR1 kinase inhibitors is being pursued. The present results confirm predictions of computational modeling that an aryl subtituent can be introduced at the 2-position in strucure 3. The substituent is anticipated to project deeper into the binding site and provide opportunities for enhanced activity and selectivity. The most potent analog reported here, 13, has a 4-hydroxyphenyl substituent and yields an IC(50) of 6 μM for inhibition of phosphorylation by FGFR1 kinase. It was also found that the western anisole-containing substituent in 3 can be replaced by a propionic acid group with no loss in potency and with potentially significant gains in pharmacologically relevant properties.

A novel mode of protein kinase inhibition exploiting hydrophobic motifs of autoinhibited kinases: discovery of ATP-independent inhibitors of fibroblast growth factor receptor

Protein kinase inhibitors with enhanced selectivity can be designed by optimizing binding interactions with less conserved inactive conformations because such inhibitors will be less likely to compete with ATP for binding and therefore may be less impacted by high intracellular concentrations of ATP. Analysis of the ATP-binding cleft in a number of inactive protein kinases, particularly in the autoinhibited conformation, led to the identification of a previously undisclosed non-polar region in this cleft. This ATP-incompatible hydrophobic region is distinct from the previously characterized hydrophobic allosteric back pocket, as well as the main pocket. Generalized hypothetical models of inactive kinases were constructed and, for the work described here, we selected the fibroblast growth factor receptor (FGFR) tyrosine kinase family as a case study. Initial optimization of a FGFR2 inhibitor identified from a library of commercial compounds was guided using structural information from the model. We describe the inhibitory characteristics of this compound in biophysical, biochemical, and cell-based assays, and have characterized the binding mode using x-ray crystallographic studies. The results demonstrate, as expected, that these inhibitors prevent activation of the autoinhibited conformation, retain full inhibitory potency in the presence of physiological concentrations of ATP, and have favorable inhibitory activity in cancer cells. Given the widespread regulation of kinases by autoinhibitory mechanisms, the approach described herein provides a new paradigm for the discovery of inhibitors by targeting inactive conformations of protein kinases.

The role of computational methods in the identification of bioactive compounds

Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.

Synthesis of purin-2-yl and purin-6-yl-aminoglucitols as C-nucleosidic ATP mimics and biological evaluation as FGFR3 inhibitors

Two new series of C-nucleosidic ATP mimics have been synthesized using an efficient and versatile synthetic pathway. These compounds were designed as FGFR3 inhibitors using purine as a central scaffold. The two substituents, a polyhydroxylated ribose mimic and a lipophilic moiety, were linked either in position 2 or 6 of the purine ring in order to explore any possible binding mode. All the compounds were able to inhibit FGFR3 kinase activity at a concentration of 50 μM. Unexpectedly, the best inhibitor was found to be one of the synthetic intermediates 13 bearing an iodine atom in position 2. Docking studies have confirmed its location in the ATP binding site and revealed halogen bonding among key interactions.

Virtual screening based identification of novel small-molecule inhibitors targeted to the HIV-1 capsid

The hydrophobic cavity of the C-terminal domain (CTD) of HIV-1 capsid has been recently validated as potential target for antiviral drugs by peptide-based inhibitors; however, there is no report yet of any small molecule compounds that target this hydrophobic cavity. In order to fill this gap and discover new classes of ant-HIV-1 inhibitors, we undertook a docking-based virtual screening and subsequent analog search, and medicinal chemistry approaches to identify small molecule inhibitors against this target. This article reports for the first time, to the best of our knowledge, identification of diverse classes of inhibitors that efficiently inhibited the formation of mature-like viral particles verified under electron microscope (EM) and showed potential as anti-HIV-1 agents in a viral infectivity assay against a wide range of laboratory-adapted as well as primary isolates in MT-2 cells and PBMC. In addition, the virions produced after the HIV-1 infected cells were treated with two of the most active compounds showed drastically reduced infectivity confirming the potential of these compounds as anti-HIV-1 agents. We have derived a comprehensive SAR from the antiviral data. The SAR analyses will be useful in further optimizing the leads to potential anti-HIV-1 agents.