Tetrahydropyridine LIMK inhibitors: Structure activity studies and biological characterization.

LIM Kinases, LIMK1 and LIMK2, have become promising targets for the development of inhibitors with potential application for the treatment of several major diseases. LIMKs play crucial roles in cytoskeleton remodeling as downstream effectors of small G proteins of the Rho-GTPase family, and as major regulators of cofilin, an actin depolymerizing factor. In this article we describe the conception, synthesis, and biological evaluation of novel tetrahydropyridine pyrrolopyrimidine LIMK inhibitors. Homology models were first constructed to better understand the binding mode of our preliminary compounds and to explain differences in biological activity. A library of over 60 products was generated and in vitro enzymatic activities were measured in the mid to low nanomolar range. The most promising derivatives were then evaluated in cell on cofilin phosphorylation inhibition which led to the identification of 52 which showed excellent selectivity for LIMKs in a kinase selectivity panel. We also demonstrated that 52 affected the cell cytoskeleton by disturbing actin filaments. Cell migration studies with this derivative using three different cell lines displayed a significant effect on cell motility. Finally, the crystal structure of the kinase domain of LIMK2 complexed with 52 was solved, greatly improving our understanding of the interaction between 52 and LIMK2 active site. The reported data represent a basis for the development of more efficient LIMK inhibitors for future in vivo preclinical validation.

Quantifying Functional-Group-like Structural Fragments in Molecules and Its Applications in Drug Design.

A functional group in a molecule is a structural fragment consisting of a few atoms or a single atom that imparts reactivity to a molecule. Hence, defining functional groups is crucial in chemistry to predict the properties and reactivities of molecules. However, there is no established method in the literature for defining functional groups based on reactivity parameters. In this work, we addressed this issue by designing a set of predefined structural fragments along with reactivity parameters like electron conjugation and ring strain. This approach uses bond orders and atom connectivities to quantify the presence of these fragments within an organic molecule based on a given input molecular coordinate. To assess the effectiveness of this approach, we performed a case study to show the benefits of using these newly designed structural fragments instead of traditional fingerprint-based methods for grouping potential COX1/COX2 inhibitors by screening an approved drug library against aspirin molecule. The structural fragment-based model for ternary classification of rat oral LD50 of chemicals showed performance similar to the fingerprint-based models. In evaluating the regression model performance for aqueous solubility, log(S), predictions, our approach outperformed the fingerprint-based model.

Second-Generation CD73 Inhibitors Based on a 4,6-Biaryl-2-thiopyridine Scaffold.

Various series of 4,6-biaryl-2-thiopyridine derivatives were synthesized and evaluated as potential ecto-5'-nucleotidase (CD73) inhibitors. Two synthetic routes were explored and the coupling of 4,6-disubstituted 3-cyano-2-chloro-pyridines with selected thiols allowed us to explore the structural diversity. Somehow divergent results were obtained in biological assays on CD73 inhibition using either the purified recombinant protein or cell-based assays, highlighting the difficulty to target protein-protein interface on proteins existing as soluble and membrane-bound forms. Among the 18 new derivatives obtained, three derivatives incorporating morpholino substituents on the 4,6-biaryl-2-thiopyridine core were shown to be able to reverse the adenosine-mediated immune suppression on human T cells. The higher blockade efficiency was observed for 2-((3-cyano-4,6-bis(4-morpholinophenyl)pyridin-2-yl)thio)-N-(isoxazol-3-yl)acetamide (with total reversion at 100 µM) and methyl 2-((3-cyano-4,6-bis(4-morpholinophenyl)pyridin-2-yl)thio)acetate (with partial reversion at 10 µM). Thus, this series of compounds illustrates a new chemotype of CD73 allosteric inhibitors.

Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules.

Force fields for drug-like small molecules play an essential role in molecular dynamics simulations and binding free energy calculations. In particular, the accurate generation of partial charges on small molecules is critical to understanding the interactions between proteins and drug-like molecules. However, it is a time-consuming process. Thus, we generated a force field for small molecules and employed a machine learning (ML) model to rapidly predict partial charges on molecules in less than a minute of time. We performed density functional theory (DFT) calculation for 31770 small molecules that covered the chemical space of drug-like molecules. The partial charges for the atoms in a molecule were predicted using an ML model trained on DFT-based atomic charges. The predicted values were comparable to the charges obtained from DFT calculations. The ML model showed high accuracy in the prediction of atomic charges for external test data sets. We also developed neural network (NN) models to assign atom types, phase angles and periodicities. All the models performed with high accuracy on test data sets. Our code calculated all the descriptors that were needed for the prediction of force field parameters and produced topologies for small molecules by combining results from ML and NN models. To assess the accuracy of the predicted force field parameters, we calculated solvation free energies for small molecules, and the results were in close agreement with experimental free energies. The AI-generated force field was effective in the fast and accurate generation of partial charges and other force field parameters for small drug-like molecules.

Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors.

Recently, academic and industrial scientific communities involved in kinetics-based drug development have become immensely interested in predicting the drug target residence time. Screening drug candidates in terms of their computationally predicted residence times, which is a measure of drug efficacy in vivo, and simultaneously assessing computational binding affinities are becoming inevitable. Non-equilibrium molecular simulation approaches are proven to be useful in this purpose. Here, we have implemented an optimized approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model to predict the absolute residence times of the antagonist ZMA241385 and agonist NECA that target the A2A adenosine receptor of the G-protein-coupled receptor (GPCR) protein family. We have predicted the absolute ligand residence times on the timescale of seconds. However, our predictions were many folds shorter than those determined experimentally. Additionally, we calculated the thermodynamics of ligand binding in terms of ligand binding energies and the per-residue contribution of the receptor. Subsequently, binding pocket hotspot residues that would be important for further computational mutagenesis studies were identified. In the experiment, similar sets of residues were found to be in significant contact with both ligands under study. Our results build a strong foundation for further improvement of our approach by rationalizing the kinetics of ligand unbinding with the thermodynamics of ligand binding.

4-Substituted-1,2,3-triazolo nucleotide analogues as CD73 inhibitors, their synthesis, in vitro screening, kinetic and in silico studies.

Three series of nucleotide analogues were synthesized and evaluated as potential CD73 inhibitors. Nucleobase replacement consisted in connecting the appropriate aromatic or purine residues through a triazole moiety that is generated from 1,3-dipolar cycloaddition. The first series is related to 4-substituted-1,2,3-triazolo-ß-hydroxyphosphonate ribonucleosides. Additional analogues were also obtained, in which the phosphonate group was replaced by a bisphosphonate pattern (P-C-P-C, series 2) or the ribose moiety was removed leading to acyclic derivatives (series 3). The ß-hydroxyphosphonylphosphonate ribonucleosides (series 2) were found to be potent inhibitors of CD73 using both purified recombinant protein and cell-based assays. Two compounds (2a and 2b) that contained a bis(trifluoromethyl)phenyl or a naphthyl substituents proved to be the most potent inhibitors, with IC50 values of 4.8 ± 0.8 µM and 0.86 ± 0.2 µM, compared to the standard AOPCP (IC50 value of 3.8 ± 0.9 µM), and were able to reverse the adenosine-mediated immune suppression on human T cells. This series of compounds illustrates a new type of CD73 inhibitors.

Residence Time Prediction of Type 1 and 2 Kinase Inhibitors from Unbinding Simulations.

In the early stage of a drug discovery process, the selection and optimization of a ligand is mainly based on equilibrium thermodynamic constants such as KD or IC50 values, which are representatives of the affinity of the compound for its target. However, these criteria are not able to correctly evaluate the efficacy of compounds in vivo and result in many failures of compound development during phase II of clinical trials. Residence time (RT) is an important parameter associated to an in vivo drug's safety and efficacy. The determination or modulation of kinetic rates correlated to RT may be performed to identify the best drug candidates in the early stages of a drug design project. For this purpose, a number of experimental methodologies were developed but remain costly in both time and money. Herein, we developed a novel protocol based on biased molecular dynamics simulations and transition-state theory in order to predict relative ligand kinetic rates and relative RTs of a series of compounds. First, we have repeatedly simulated the unbinding process of the ligand from its binding site to the outside of the target. Next, we sample the conformational space along the determined unbinding paths to allow relevant statistical distributions of the system. The free energy profiles associated to these distributions are then computed and used to predict the kinetics parameters. The studied set was composed of eight ligands with fast, intermediate, and slow dissociation rates and binding to the active and inactive states of p38α protein kinase. The proposed method provides an excellent correlation between the predicted values and the experimentally measured kinetic rates, in addition to a detailed characterization of the kinetic paths at the atomic level.

Enhanced Molecular Dynamics Methods Applied to Drug Design Projects.

Nobel Laureate Richard P. Feynman stated: "[…] everything that living things do can be understood in terms of jiggling and wiggling of atoms […]." The importance of computer simulations of macromolecules, which use classical mechanics principles to describe atom behavior, is widely acknowledged and nowadays, they are applied in many fields such as material sciences and drug discovery. With the increase of computing power, molecular dynamics simulations can be applied to understand biological mechanisms at realistic timescales. In this chapter, we share our computational experience providing a global view of two of the widely used enhanced molecular dynamics methods to study protein structure and dynamics through the description of their characteristics, limits and we provide some examples of their applications in drug design. We also discuss the appropriate choice of software and hardware. In a detailed practical procedure, we describe how to set up, run, and analyze two main molecular dynamics methods, the umbrella sampling (US) and the accelerated molecular dynamics (aMD) methods.

Advanced molecular dynamics simulation methods for kinase drug discovery.

Interest in the application of molecular dynamics (MD) simulations has increased in the field of protein kinase (PK) drug discovery. PKs belong to an important drug target class because they are directly involved in a number of diseases, including cancer. MD methods simulate dynamic biological and chemical events at an atomic level. This information can be combined with other in silico and experimental methods to efficiently target selected receptors. In this review, we present common and advanced methods of MD simulations and we focus on the recent applications of MD-based methodologies that provided significant insights into the elucidation of biological mechanisms involving PKs and into the discovery of novel kinase inhibitors.