3-Aminopyrrolidinone farnesyltransferase inhibitors: design of macrocyclic compounds with improved pharmacokinetics and excellent cell potency

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Incorporation of Perillyl Alcohol into Lipid-Based Nanocarriers Enhances the Antiproliferative Activity in Malignant Glioma Cells

Perillyl alcohol (PA), a naturally existing monocyclic terpene related to limonene, is characterized by its poor aqueous solubility and very limited bioavailability. Its potential anti-cancer activity against malignant glioma has been reported. The aim was to develop PA-loaded lipid-based nanocarriers (LNCs), and to investigate their anti-cancer activity against two different brain cell lines. Non-medicated and PA-loaded LNCs were prepared and characterized. The mechanism of cytotoxic activity of PA was conducted using a molecular docking technique. The cell viabilities against A172 and ANGM-CSS cells were evaluated. The results revealed that the average particle size of the prepared LNCs ranged from 248.67 ± 12.42 to 1124.21 ± 12.77 nm, the polydispersity index was 0.418 ± 0.043-0.509 ± 0.064, while the zeta potential ranged from -36.91 ± 1.31 to -15.20 ± 0.96 mV. The molecular docking studies demonstrated that the drug had binding activity to human farnesyltransferase. Following exposure of the two glioblastoma cell lines to the PA-loaded nanoformulations, MTS assays were carried out, and the data showed a far lower half-maximal inhibitory concentration in both cell lines when compared to pure drug and non-medicated nanocarriers. These results indicate the potential in vitro antiproliferative activity of PA-loaded LNCs. Therefore, the prepared PA-loaded nanocarriers could be used to enhance drug delivery across the blood-brain barrier (BBB) in order to treat brain cancer, especially when formulated in a suitable dosage form. The size, surface charge, and lipid composition of the LNCs make them promising for drug delivery across the BBB. Detailed pharmacokinetic and pharmacodynamic assessments, including the evaluation of BBB penetration, are necessary to better understand the compound's distribution and effects within the brain.

High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment

Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.

Phenothiazine- and Carbazole-Cyanochalcones as Dual Inhibitors of Tubulin Polymerization and Human Farnesyltransferase

In the search for innovative approaches to cancer chemotherapy, a chemical library of 49 cyanochalcones, 1a-r, 2a-o, and 3a-p, was designed as dual inhibitors of human farnesyltransferase (FTIs) and tubulin polymerization (MTIs) (FTIs/MTIs), two important biological targets in oncology. This approach is innovative since the same molecule would be able to interfere with two different mitotic events of the cancer cells and prevent these cells from developing an emergency route and becoming resistant to anticancer agents. Compounds were synthesized by the Claisen-Schmidt condensation of aldehydes with N-3-oxo-propanenitriles under classical magnetic stirring and under sonication. Newly synthesized compounds were screened for their potential to inhibit human farnesyltransferase, tubulin polymerization, and cancer cell growth in vitro. This study allowed for the identification of 22 FTIs and 8 dual FTIs/MTIs inhibitors. The most effective molecule was carbazole-cyanochalcone 3a, bearing a 4-dimethylaminophenyl group (IC₅₀ (h-FTase) = 0.12 µM; IC₅₀ (tubulin) = 0.24 µM) with better antitubulin activity than the known inhibitors that were previously reported, phenstatin and (-)-desoxypodophyllotoxin. The docking of the dual inhibitors was realized in both the active site of FTase and in the colchicine binding site of tubulin. Such compounds with a dual inhibitory profile are excellent clinical candidates for the treatment of human cancers and offer new research perspectives in the search for new anti-cancer drugs.

Structural analysis of hERG channel blockers and the implications for drug design

The repolarizing current (I_kr) produced by the hERG potassium channel forms a major component of the cardiac action potential and blocking this current by small molecule drugs can lead to life-threatening cardiotoxicity. Understanding the mechanisms of drug-mediated hERG inhibition is essential to develop a second generation of safe drugs, with minimal cardiotoxic effects. Although various computational tools and drug design guidelines have been developed to avoid binding of drugs to the hERG pore domain, there are many other aspects that are still open for investigation. This includes the use computational modelling to study the implications of hERG mutations on hERG structure and trafficking, the interactions of hERG with hERG chaperone proteins and with membrane-soluble molecules, the mechanisms of drugs that inhibit hERG trafficking and drugs that rescue hERG mutations. The plethora of available experimental data regarding all these aspects can guide the construction of much needed robust computational structural models to study these mechanisms for the rational design of safe drugs.

Cyclization and Docking Protocol for Cyclic Peptide-Protein Modeling Using HADDOCK2.4

An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design, but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclization and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to the critical assessment of prediction of interaction criteria for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top 10) both in the bound and unbound docking scenarios. Moreover, its performance in both bound and fully unbound docking is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclization and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening.

Computational studies of molecular pre-organization through macrocyclization: Conformational distribution analysis of closely related non-macrocyclic and macrocyclic analogs

Macrocycles form an important compound class in medicinal chemistry due to their interesting structural and biological properties. To help design macrocycles, it is important to understand how the conformational preferences are affected upon macrocyclization of a lead compound. To address this, we collected a unique data set of protein-ligand complexes containing "non-macrocyclic" ("linear") ligands matched with macrocyclic analogs binding to the same protein in a similar pose. Out of the 39 co-crystallized ligands considered, 10 were linear and 29 were macrocyclic. To enable a more general analysis, 128 additional ligands from the publications associated with these protein data bank entries were added to the data set. Using in total 167 collected ligands, we investigated if the conformers in the macrocyclic conformational ensembles were more similar to the bioactive conformation in comparison to the conformers of their linear counterparts. Unexpectedly, in most cases the macrocycle conformational ensemble distributions were not very different from those of the linear compounds. Thus, care should be taken when designing macrocycles with the aim to focus their conformational preference towards the bioactive conformation. We also set out to investigate potential conformational flexibility differences between the two compound classes, computational energy window settings and evaluate a literature metric for approximating the conformational focusing on the bioactive conformation.

Benchmark of Generic Shapes for Macrocycles

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional “basic knowledge” alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.

Macrocycle Cell Permeability Measured by Solvation Free Energies in Polar and Apolar Environments

The relation of surface polarity and conformational preferences is decisive for cell permeability and thus bioavailability of macrocyclic drugs. Here, we employ grid inhomogeneous solvation theory (GIST) to calculate solvation free energies for a series of six macrocycles in water and chloroform as a measure of passive membrane permeability. We perform accelerated molecular dynamics simulations to capture a diverse structural ensemble in water and chloroform, allowing for a direct profiling of solvent-dependent conformational preferences. Subsequent GIST calculations facilitate a quantitative measure of solvent preference in the form of a transfer free energy, calculated from the ensemble-averaged solvation free energies in water and chloroform. Hence, the proposed method considers how the conformational diversity of macrocycles in polar and apolar solvents translates into transfer free energies. Following this strategy, we find a striking correlation of 0.92 between experimentally determined cell permeabilities and calculated transfer free energies. For the studied model systems, we find that the transfer free energy exceeds the purely water-based solvation free energies as a reliable estimate of cell permeability and that conformational sampling is imperative for a physically meaningful model. We thus recommend this purely physics-based approach as a computational tool to assess cell permeabilities of macrocyclic drug candidates.

hERG toxicity assessment: Useful guidelines for drug design

All along the drug development process, one of the most frequent adverse side effects, leading to the failure of drugs, is the cardiac arrhythmias. Such failure is mostly related to the capacity of the drug to inhibit the human ether-à-go-go-related gene (hERG) cardiac potassium channel. The early identification of hERG inhibition properties of biological active compounds has focused most of attention over the years. In order to prevent the cardiac side effects, a great number of in silico, in vitro and in vivo assays have been performed. The main goal of these studies is to understand the reasons of these effects, and then to give information or instructions to scientists involved in drug development to avoid the cardiac side effects. To evaluate anticipated cardiovascular effects, early evaluation of hERG toxicity has been strongly recommended for instance by the regulatory agencies such as U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Thus, following an initial screening of a collection of compounds to find hits, a great number of pharmacomodulation studies on the novel identified chemical series need to be performed including activity evaluation towards hERG. We provide in this concise review clear guidelines, based on described examples, illustrating successful optimization process to avoid hERG interactions as cases studies and to spur scientists to develop safe drugs.

Conformational analysis of macrocycles: comparing general and specialized methods

Abstract

Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-ray_ppw), and (v) to the X-ray_ppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-ray_ppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-ray_ppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-ray_ppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima.

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s10822-020-00277-2) contains supplementary material, which is available to authorized users.

How Significant Are Unusual Protein-Ligand Interactions? Insights from Database Mining

We present a new approach to derive interaction propensities of protein-ligand atom pairs from mining of the Protein Data Bank. To ensure solid statistics, we use a line-of-sight contact filter and normalize the observed frequency of hits by a statistical null model based on exposed surface areas of atom types in the protein-ligand binding site. This allows us to investigate which intermolecular interactions and geometries are found more often than expected by chance in protein-ligand complexes. We focus our study on some of the unusual interactions that were postulated to be favorable, including σ-hole bonding of halogen and sulfur atoms, weak hydrogen bonding with fluorine as acceptor, and different types of dipolar interactions. Our results confirm some and challenge other common assumptions on these interactions and highlight other contact types that are yet underexplored in structure-based drug design.

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization

Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble. By applying an energetic cutoff, followed by geometric clustering, we demonstrate the striking robustness and efficiency of the approach in identifying highly populated conformational states of cyclic peptides. The resulting structural and thermodynamic information is benchmarked against interproton distances from NMR experiments and conformational states identified by X-ray crystallography. Using three different model systems of varying size and flexibility, we show that the method reliably reproduces experimentally determined structural ensembles and is capable of identifying key conformational states that include the bioactive conformation. Thus, the described approach is a robust method to generate conformations of peptidic macrocycles and holds promise for structure-based drug design.

Computational Macrocyclization: From de novo Macrocycle Generation to Binding Affinity Estimation

Macrocycles play an increasing role in drug discovery, but their synthesis is often demanding. Computational tools that suggest macrocyclization based on a known binding mode and that estimate the binding affinity of these macrocycles could have a substantial impact on the medicinal chemistry design process. For both tasks, we established a workflow with high practical value. For five diverse pharmaceutical targets we show that the effect of macrocyclization on binding can be calculated robustly and accurately. Applying this method to macrocycles designed by LigMac, a search tool for de novo macrocyclization, our results suggest that we have a robust protocol in hand to design macrocycles and prioritize them prior to synthesis.

Gene expression analysis in response to osmotic stimuli in the intervertebral disc with DNA microarray

Background

Intervertebral disc (IVD) cells experience a broad range of physicochemical stimuli under physiologic conditions, including alterations in their osmotic environment. At present, the molecular mechanisms underlying osmotic regulation in IVD cells are poorly understood. This study aims to screen genes affected by changes in osmotic pressure in cells of subjects aged 29 to 63 years old, with top-scoring pair (TSP) method.

Methods

Gene expression data set GSE1648 was downloaded from Gene Expression Omnibus database, including four hyper-osmotic stimuli samples, four iso-osmotic stimuli samples, and three hypo-osmotic stimuli samples. A novel, simple method, referred to as the TSP, was used in this study. Through this method, there was no need to perform data normalization and transformation before data analysis.

Results

A total of five pairs of genes ((CYP2A6, FNTB), (PRPF8, TARDBP), (RPS5, OAZ1), (SLC25A3, NPM1) and (CBX3, SRSF9)) were selected based on the TSP method. We inferred that all these genes might play important roles in response to osmotic stimuli and age in IVD cells. Additionally, hyper-osmotic and iso-osmotic stimuli conditions were adverse factors for IVD cells.

Conclusions

We anticipate that our results will provide new thoughts and methods for the study of IVD disease.

Removal of human ether-à-go-go related gene (hERG) K+ channel affinity through rigidity: a case of clofilium analogues

Cardiotoxicity is a side effect that plagues modern drug design and is very often due to the off-target blockade of the human ether-à-go-go related gene (hERG) potassium channel. To better understand the structural determinants of this blockade, we designed and synthesized a series of 40 derivatives of clofilium, a class III antiarrhythmic agent. These were evaluated in radioligand binding and patch-clamp assays to establish structure-affinity relationships (SAR) for this potassium channel. Efforts were especially focused on studying the influence of the structural rigidity and the nature of the linkers composing the clofilium scaffold. It was shown that introducing triple bonds and oxygen atoms in the n-butyl linker of the molecule greatly reduced affinity without significantly modifying the pKa of the essential basic nitrogen. These findings could prove useful in the first stages of drug discovery as a systematic way of reducing the risk of hERG K(+) channel blockade-induced cardiotoxicity.

Role of protein farnesylation events in the ABA-mediated regulation of the Pinoresinol-Lariciresinol Reductase 1 (LuPLR1) gene expression and lignan biosynthesis in flax (Linum usitatissimum L.)

A Linum usitatissimum LuERA1 gene encoding a putative ortholog of the ERA1 (Enhanced Response to ABA 1) gene of Arabidopsis thaliana (encoding the beta subunit of a farnesyltransferase) was analyzed in silico and for its expression in flax. The gene and the protein sequences are highly similar to other sequences already characterized in plants and all the features of a farnesyltransferase were detected. Molecular modeling of LuERA1 protein confirmed its farnesyltransferase nature. LuERA1 is expressed in the vegetative organs and also in the outer seedcoat of the flaxseed, where it could modulate the previously observed regulation operated by ABA on lignan synthesis. This effect could be mediated by the regulation of the transcription of a key gene for lignan synthesis in flax, the LuPLR1 gene, encoding a pinoresinol lariciresinol reductase. The positive effect of manumycin A, a specific inhibitor of farnesyltransferase, on lignan biosynthesis in flax cell suspension systems supports the hypothesis of the involvement of such an enzyme in the negative regulation of ABA action. In Arabidopsis, ERA1 is able to negatively regulate the ABA effects and the mutant era1 has an enhanced sensitivity to ABA. When expressed in an Arabidopsis cell suspension (heterologous system) LuERA1 is able to reverse the effect of the era1 mutation. RNAi experiments in flax targeting the farnesyltransferase β-subunit encoded by the LuERA1 gene led to an increase LuPLR1 expression level associated with an increased content of lignan in transgenic calli. Altogether these results strongly suggest a role of the product of this LuERA1 gene in the ABA-mediated upregulation of lignan biosynthesis in flax cells through the activation of LuPLR1 promoter. This ABA signaling pathway involving ERA1 probably acts through the ABRE box found in the promoter sequence of LuPLR1, a key gene for lignan synthesis in flax, as demonstrated by LuPLR1 gene promoter-reporter experiments in flax cells using wild type and mutated promoter sequences.

Inverse virtual screening of antitumor targets: pilot study on a small database of natural bioactive compounds

An inverse virtual screening in silico approach has been applied to natural bioactive molecules to screen their efficacy against proteins involved in cancer processes, with the aim of directing future experimental assays. Docking studies were performed on a panel of 126 protein targets extracted from the Protein Data Bank, to analyze their possible interactions with a small library of 43 bioactive compounds. Analysis of the molecular docking results was performed through the use of tables containing energy data organized in a matrix. The application of this approach may facilitate the prediction of the activity of unknown ligands for known targets involved in the development of cancer and could be applied to other models based on different libraries of ligands and different panels of targets.

Stereoselective Inhibition of the hERG1 Potassium Channel

A growing number of drugs have been shown to prolong cardiac repolarization, predisposing individuals to life-threatening ventricular arrhythmias known as Torsades de Pointes. Most of these drugs are known to interfere with the human ether à-gogo related gene 1 (hERG1) channel, whose current is one of the main determinants of action potential duration. Prolonged repolarization is reflected by lengthening of the QT interval of the electrocardiogram, as seen in the suitably named drug-induced long QT syndrome. Chirality (presence of an asymmetric atom) is a common feature of marketed drugs, which can therefore exist in at least two enantiomers with distinct three-dimensional structures and possibly distinct biological fates. Both the pharmacokinetic and pharmacodynamic properties can differ between enantiomers, as well as also between individuals who take the drug due to metabolic polymorphisms. Despite the large number of reports about drugs reducing the hERG1 current, potential stereoselective contributions have only been scarcely investigated. In this review, we present a non-exhaustive list of clinically important molecules which display chiral toxicity that may be related to hERG1-blocking properties. We particularly focus on methadone cardiotoxicity, which illustrates the importance of the stereoselective effect of drug chirality as well as individual variations resulting from pharmacogenetics. Furthermore, it seems likely that, during drug development, consideration of chirality in lead optimization and systematic assessment of the hERG1 current block with all enantiomers could contribute to the reduction of the risk of drug-induced LQTS.

Computer-aided drug design and ADMET predictions for identification and evaluation of novel potential farnesyltransferase inhibitors in cancer therapy

We have used various computational methodologies including molecular dynamics, density functional theory, virtual screening, ADMET predictions and molecular interaction field studies to design and analyze four novel potential inhibitors of farnesyltransferase (FTase). Evaluation of two proposals regarding their drug potential as well as lead compounds have indicated them as novel promising FTase inhibitors, with theoretically interesting pharmacotherapeutic profiles, when compared to the very active and most cited FTase inhibitors that have activity data reported, which are launched drugs or compounds in clinical tests. One of our two proposals appears to be a more promising drug candidate and FTase inhibitor, but both derivative molecules indicate potentially very good pharmacotherapeutic profiles in comparison with Tipifarnib and Lonafarnib, two reference pharmaceuticals. Two other proposals have been selected with virtual screening approaches and investigated by us, which suggest novel and alternatives scaffolds to design future potential FTase inhibitors. Such compounds can be explored as promising molecules to initiate a research protocol in order to discover novel anticancer drug candidates targeting farnesyltransferase, in the fight against cancer.

Structural basis for binding and selectivity of antimalarial and anticancer ethylenediamine inhibitors to protein farnesyltransferase

Protein farnesyltransferase (FTase) catalyzes an essential posttranslational lipid modification of more than 60 proteins involved in intracellular signal transduction networks. FTase inhibitors have emerged as a significant target for development of anticancer therapeutics and, more recently, for the treatment of parasitic diseases caused by protozoan pathogens, including malaria (Plasmodium falciparum). We present the X-ray crystallographic structures of complexes of mammalian FTase with five inhibitors based on an ethylenediamine scaffold, two of which exhibit over 1000-fold selective inhibition of P. falciparum FTase. These structures reveal the dominant determinants in both the inhibitor and enzyme that control binding and selectivity. Comparison to a homology model constructed for the P. falciparum FTase suggests opportunities for further improving selectivity of a new generation of antimalarial inhibitors.

Inhibition of farnesyltransferase: a rational approach to treat cancer?

This article presents in brief the development of farnesyltransferase inhibitors (FTIs) and their preclinical and clinical status. In this review the mechanism of action of FTIs is discussed and their selectivity issue towards tumor cells is also addressed. The significant efficacy of FTIs as single or combined agents in preclinical studies stands in contrast with only moderate effects in Clinical Phase II-III studies. This suggests that there is a need to further explore and understand the complex mechanism of action of FTIs and their interaction with cytotoxic agents.

Design, synthesis, and SAR of macrocyclic tertiary carbinamine BACE-1 inhibitors

This Letter describes the design and synthesis of tertiary carbinamine macrocyclic inhibitors of the beta-secretase (BACE-1) enzyme. These macrocyclic inhibitors, some of which incorporate novel P2 substituents, display a 2- to 100-fold increase in potency relative to the previously described acyclic analogs while affording greater stability.

Synthesis and primary cytotoxicity evaluation of arylmethylenenaphthofuranones derivatives

New series of 2(or 3)-arylmethylenenaphtho[2,1-b]furan-3(or 2)-ones were synthesized, characterized and tested for anticancer properties in vitro. The target compounds were prepared by Knoevenagel coupling between the naphthofuranones 3, 28-30 and formyl derivatives. 2-(4-Oxo-1-benzopyran-3-ylmethylene)naphtho[2,1-b]furan-3-one 36 was the most active compound (IC50 (L1210) = 1.6 microM). These compounds were also evaluated, in an independent manner, as inhibitors of Src protein tyrosine kinase, but only minor activity was observed.

Solid-phase synthesis and pharmacological evaluation of a library of peptidomimetics as potential farnesyltransferase inhibitors: an approach to new lead compounds

Oncogenic Ras proteins whose activation is farnesylation by farnesyltransferase have been seen as important targets for novel anticancer drugs. Inhibitors of this enzyme have already been developed as potential anti-cancer drugs, particularly by rational design based on the structure of the CA(1)A(2)X carboxyl terminus of Ras. Synthesis of a peptidomimetics library via solid-phase synthesis using the Multipin method is described here. The most active hits on cellular assays were resynthesized and enzymatic activity was measured. Compounds A1, A5 and A7 present significant activity on the isolated enzyme (IC(50)=117, 57.3 and 28.5 nM) and their molecular docking in the active site of the enzyme provides details on key interactions with the protein.

Thematic review series: lipid posttranslational modifications. Structural biology of protein farnesyltransferase and geranylgeranyltransferase type I

More than 100 proteins necessary for eukaryotic cell growth, differentiation, and morphology require posttranslational modification by the covalent attachment of an isoprenoid lipid (prenylation). Prenylated proteins include members of the Ras, Rab, and Rho families, lamins, CENPE and CENPF, and the gamma subunit of many small heterotrimeric G proteins. This modification is catalyzed by the protein prenyltransferases: protein farnesyltransferase (FTase), protein geranylgeranyltransferase type I (GGTase-I), and GGTase-II (or RabGGTase). In this review, we examine the structural biology of FTase and GGTase-I (the CaaX prenyltransferases) to establish a framework for understanding the molecular basis of substrate specificity and mechanism. These enzymes have been identified in a number of species, including mammals, fungi, plants, and protists. Prenyltransferase structures include complexes that represent the major steps along the reaction path, as well as a number of complexes with clinically relevant inhibitors. Such complexes may assist in the design of inhibitors that could lead to treatments for cancer, viral infection, and a number of deadly parasitic diseases.

Chemical genetics identifies Rab geranylgeranyl transferase as an apoptotic target of farnesyl transferase inhibitors

A chemical genetics approach identified a cellular target of several proapoptotic farnesyl transferase inhibitors (FTIs). Treatment with these FTIs caused p53-independent apoptosis in Caenorhabditis elegans, which was mimicked by knockdown of endosomal trafficking proteins, including Rab5, Rab7, the HOPS complex, and notably the enzyme Rab geranylgeranyl transferase (RabGGT). These FTIs were found to inhibit mammalian RabGGT with potencies that correlated with their proapoptotic activity. Knockdown of RabGGT induced apoptosis in mammalian cancer cell lines, and both RabGGT subunits were overexpressed in several tumor tissues. These findings validate RabGGT, and by extension endosomal function, as a therapeutically relevant target for modulation of apoptosis, and enhance our understanding of the mechanism of action of FTIs.

Structural analysis of DNA-PKcs: modelling of the repeat units and insights into the detailed molecular architecture

DNA-dependent protein kinase (DNA-PK) is part of the eukaryotic DNA double strand break repair pathway and as such is crucial for maintenance of genomic stability, as well as for V(D)J (variable-diversity-joining) recombination. The catalytic subunit of DNA-PK (DNA-PKcs) belongs to the phosphatidylinositol-3 (PI-3) kinase-like kinase (PIKK) superfamily and is comprised of approximately 4100 amino acids. We have used a novel repeat detection method to analyse this enormous protein and have identified two different types of helical repeat motifs in the N-terminal region of the sequence, as well as other previously unreported features in this repeat region. A comparison with the ATMs, ATRs, and TORs show that the features identified are likely to be conserved throughout the PIKK superfamily. Homology modelling of parts of the DNA-PKcs sequence has been undertaken and we have been able to fit the models to previously obtained electron microscopy data. This work provides an insight into the overall architecture of the DNA-PKcs protein and identifies regions of interest for further experimental studies.

The role of the medicinal chemist in drug discovery--then and now

The role of the medicinal chemist in drug discovery has undergone major changes in the past 25 years, mainly because of the introduction of technologies such as combinatorial chemistry and structure-based drug design. As medicinal chemists with more than 50 years of combined experience spanning the past four decades, we discuss this changing role using examples from our own and others' experience. This historical perspective could provide insights in to how to improve the current model for drug discovery by helping the medicinal chemist regain the creative role that contributed to past successes.

A fluorine scan of the phenylamidinium needle of tricyclic thrombin inhibitors: effects of fluorine substitution on pKa and binding affinity and evidence for intermolecular C-F...CN interactions

The H-atoms of the phenylamidinium needle of tricyclic thrombin inhibitors, which interacts with Asp189 at the bottom of the selectivity pocket S1 of the enzyme, were systematically exchanged with F-atoms in an attempt to improve the pharmacokinetic properties by lowering the pK(a) value. Both the pK(a) values and the inhibitory constants K(i) against thrombin and trypsin were decreased upon F-substitution. Interestingly, linear free energy relationships (LFERs) revealed that binding affinity against thrombin is much more affected by a decrease in pK(a) than the affinity against trypsin. Surprising effects of F-substitutions in the phenylamidinium needle on the pK(a) value of the tertiary amine centre in the tricyclic scaffold of the inhibitors were observed and subsequently rationalised by X-ray crystallographic analysis and ab initio calculations. Evidence for highly directional intermolecular C-F...CN interactions was obtained by analysis of small-molecule X-ray crystal structures and investigations in the Cambridge Structural Database (CSD).

Predicting undesirable drug interactions with promiscuous proteins in silico

Although computational tools have been used to predict toxic responses resulting from molecules binding either as substrates or inhibitors to proteins, there are complications to be resolved. Some proteins appear promiscuous in their ability to bind a diverse array of hydrophobic molecules. This promiscuity arises from the binding site simultaneously accommodating more than one molecule, multiple separate binding sites, protein flexibility, or a combination of all these properties. With the availability of more crystal structures for these non-target proteins, we should be able to predict binding in silico with a greater accuracy, thus avoiding or managing toxic side effects, therefore ultimately improving the success of drug discovery.