Riluzole-Rasagiline Hybrids: Toward the Development of Multi-Target-Directed Ligands for Amyotrophic Lateral Sclerosis

Polypharmacology is a new trend in amyotrophic lateral sclerosis (ALS) therapy and an effective way of addressing a multifactorial etiology involving excitotoxicity, mitochondrial dysfunction, oxidative stress, and microglial activation. Inspired by a reported clinical trial, we converted a riluzole (1)–rasagiline (2) combination into single-molecule multi-target-directed ligands. By a ligand-based approach, the highly structurally integrated hybrids 3–8 were designed and synthesized. Through a target- and phenotypic-based screening pipeline, we identified hit compound 6. It showed monoamine oxidase A (MAO-A) inhibitory activity (IC₅₀ = 6.9 μM) rationalized by in silico studies as well as in vitro brain permeability. By using neuronal and non-neuronal cell models, including ALS-patient-derived cells, we disclosed for 6 a neuroprotective/neuroinflammatory profile similar to that of the parent compounds and their combination. Furthermore, the unexpected MAO inhibitory activity of 1 (IC₅₀ = 8.7 μM) might add a piece to the puzzle of its anti-ALS molecular profile.

Computational strategies to identify new drug candidates against neuroinflammation

The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.

HT-SuMD: making molecular dynamics simulations suitable for fragment-based screening. A comparative study with NMR

Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-XL, an oncological protein target involved in the regulation of apoptosis through protein-protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-XL binders. This represents, to date, the largest computational fragments screening entirely based on MD.

Shedding Light on the Molecular Recognition of Sub-Kilodalton Macrocyclic Peptides on Thrombin by Supervised Molecular Dynamics

Macrocycles are attractive structures for drug development due to their favorable structural features, potential in binding to targets with flat featureless surfaces, and their ability to disrupt protein–protein interactions. Moreover, large novel highly diverse libraries of low-molecular-weight macrocycles with therapeutically favorable characteristics have been recently established. Considering the mentioned facts, having a validated, fast, and accurate computational protocol for studying the molecular recognition and binding mode of this interesting new class of macrocyclic peptides deemed to be helpful as well as insightful in the quest of accelerating drug discovery. To that end, the ability of the in-house supervised molecular dynamics protocol called SuMD in the reproduction of the X-ray crystallography final binding state of a macrocyclic non-canonical tetrapeptide—from a novel library of 8,988 sub-kilodalton macrocyclic peptides—in the thrombin active site was successfully validated. A comparable binding mode with the minimum root-mean-square deviation (RMSD) of 1.4 Å at simulation time point 71.6 ns was achieved. This method validation study extended the application domain of the SuMD sampling method for computationally cheap, fast but accurate, and insightful macrocycle–protein molecular recognition studies.

A Computational Workflow for the Identification of Novel Fragments Acting as Inhibitors of the Activity of Protein Kinase CK1δ

Fragment-Based Drug Discovery (FBDD) has become, in recent years, a consolidated approach in the drug discovery process, leading to several drug candidates under investigation in clinical trials and some approved drugs. Among these successful applications of the FBDD approach, kinases represent a class of targets where this strategy has demonstrated its real potential with the approved kinase inhibitor Vemurafenib. In the Kinase family, protein kinase CK1 isoform δ (CK1δ) has become a promising target in the treatment of different neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. In the present work, we set up and applied a computational workflow for the identification of putative fragment binders in large virtual databases. To validate the method, the selected compounds were tested in vitro to assess the CK1δ inhibition.

On the mechanism of tumor cell entry of aloe-emodin, a natural compound endowed with anticancer activity

Aloe‐emodin (1,8‐dihydroxy‐3‐[hydroxymethyl]‐anthraquinone), AE, is one of the active constituents of a number of plant species used in traditional medicine. We have previously identified, for the first time, AE as a new antitumor agent and shown that its selective in vitro and in vivo killing of neuroblastoma cells was promoted by a cell‐specific drug uptake process. However, the molecular mechanism underlying the cell entry of AE has remained elusive as yet. In this report, we show that AE enters tumor cells via two of the five somatostatin receptors: SSTR2 and SSTR5. This observation was suggested by gene silencing, receptor competition, imaging and molecular modeling experiments. Furthermore, SSTR2 was expressed in all surgical neuroblastoma specimens we analyzed by immunohistochemistry. The above findings have strong implications for the clinical adoption of this natural anthraquinone molecule as an antitumor agent.

Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 Mpro by Using Supervised Molecular Dynamics (SuMD) Simulations

Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low‐throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high‐throughput supervised molecular dynamics simulations (HT‐SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS‐CoV‐2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD‐based posing approaches, a high number of incorrect binding modes still complicate HT‐SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in‐silico pipeline, allowing us to prioritize only the more reliable predictions.

Developing novel classes of protein kinase CK1δ inhibitors by fusing [1,2,4]triazole with different bicyclic heteroaromatic systems

Protein kinase CK1δ expression and activity is involved in different pathological situations that include neuroinflammatory and neurodegenerative diseases. For this reason, protein kinase CK1δ has become a possible therapeutic target for these conditions. 5,6-fused bicyclic heteroaromatic systems that resemble adenine of ATP represent optimal scaffolds for the development of a new class of ATP competitive CK1δ inhibitors. In particular, a new series of [1,2,4]triazolo[1,5-c]pyrimidines and [1,2,4]triazolo[1,5-a][1,3,5]triazines was developed. Some crucial interactors have been identified, such as the presence of a free amino group able to interact with the residues of the hinge region at the 5- and 7- positions of the [1,2,4]triazolo[1,5-c]pyrimidine and [1,2,4]triazolo[1,5-a][1,3,5]triazine scaffolds, respectively; or the presence of a 3-hydroxyphenyl or 3,5-dihydroxyphenyl moiety at the 2- position of both nuclei. Molecular modeling studies identified the key interactions involved in the inhibitor-protein recognition process that appropriately fit with the outlined structure-activity relationship. Considering the fact that the CK1 protein kinase is involved in various pathologies in particular of the central nervous system, the interest in the development of new inhibitors permeable to the blood-brain barrier represents today an important goal in the pharmaceutical field. The best potent compound of the series is the 5-(7-amino-5-(benzylamino)-[1,2,4]triazolo[1,5-a][1,3,5]triazin-2-yl)benzen-1,3-diol (compound 51, IC₅₀ = 0.18 μM) that was predicted to have an intermediate ability to cross the membrane in our in vitro assay and represents an optimal starting point to both studies the therapeutic value of protein kinase CK1δ inhibition and to develop new more potent derivatives.

Targeting the coronavirus SARS-CoV-2: computational insights into the mechanism of action of the protease inhibitors lopinavir, ritonavir and nelfinavir

Coronavirus SARS-CoV-2 is a recently discovered single-stranded RNA betacoronavirus, responsible for a severe respiratory disease known as coronavirus disease 2019, which is rapidly spreading. Chinese health authorities, as a response to the lack of an effective therapeutic strategy, started to investigate the use of lopinavir and ritonavir, previously optimized for the treatment and prevention of HIV/AIDS viral infection. Despite the clinical use of these two drugs, no information regarding their possible mechanism of action at the molecular level is still known for SARS-CoV-2. Very recently, the crystallographic structure of the SARS-CoV-2 main protease (Mpro), also known as C30 Endopeptidase, was published. Starting from this essential structural information, in the present work we have exploited supervised molecular dynamics, an emerging computational technique that allows investigating at an atomic level the recognition process of a ligand from its unbound to the final bound state. In this research, we provided molecular insight on the whole recognition pathway of Lopinavir, Ritonavir, and Nelfinavir, three potential C30 Endopeptidase inhibitors, with the last one taken into consideration due to the promising in-vitro activity shown against the structurally related SARS-CoV protease.

The rise of molecular simulations in fragment-based drug design (FBDD): an overview

Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (μM) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments. The recent development of computational fragment-based approaches provides a further strategy for improving the identification of fragment hits. In this review, we summarize the state of the art of molecular dynamics simulations approaches used in FBDD, and discuss limitations and future perspectives for these approaches.

Scaffold Repurposing of in-House Chemical Library toward the Identification of New Casein Kinase 1 δ Inhibitors

Recent studies have highlighted the key role of Casein kinase 1 δ (CK1δ) in the development of several neurodegenerative pathologies, such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). So far, CK1δ inhibitors are noncovalent ATP competitive ligands and no drugs are currently available for this molecular target, hence the interest in developing new CK1δ inhibitors. The study aims to identify new inhibitors able to bind the enzyme; by a dual approach in silico/in vitro, the virtual screening has been performed on an in-house chemical library, which was previously designed and synthesized for other targets. The work can, therefore, be seen in the scaffold repurposing logic. The proposed strategy has led to the identification of two hits, having a novel scaffold in the landscape of CK1δ inhibitors and with an activity in the micromolar range.

Exploring the RNA-Recognition Mechanism Using Supervised Molecular Dynamics (SuMD) Simulations: Toward a Rational Design for Ribonucleic-Targeting Molecules?

Although proteins have represented the molecular target of choice in the development of new drug candidates, the pharmaceutical importance of ribonucleic acids has gradually been growing. The increasing availability of structural information has brought to light the existence of peculiar three-dimensional RNA arrangements, which can, contrary to initial expectations, be recognized and selectively modulated through small chemical entities or peptides. The application of classical computational methodologies, such as molecular docking, for the rational development of RNA-binding candidates is, however, complicated by the peculiarities characterizing these macromolecules, such as the marked conformational flexibility, the singular charges distribution, and the relevant role of solvent molecules. In this work, we have thus validated and extended the applicability domain of SuMD, an all-atoms molecular dynamics protocol that allows to accelerate the sampling of molecular recognition events on a nanosecond timescale, to ribonucleotide targets of pharmaceutical interest. In particular, we have proven the methodological ability by reproducing the binding mode of viral or prokaryotic ribonucleic complexes, as well as that of artificially engineered aptamers, with an impressive degree of accuracy.

Deciphering the Molecular Recognition Mechanism of Multidrug Resistance Staphylococcus aureus NorA Efflux Pump Using a Supervised Molecular Dynamics Approach

The use and misuse of antibiotics has resulted in critical conditions for drug-resistant bacteria emergency, accelerating the development of antimicrobial resistance (AMR). In this context, the co-administration of an antibiotic with a compound able to restore sufficient antibacterial activity may be a successful strategy. In particular, the identification of efflux pump inhibitors (EPIs) holds promise for new antibiotic resistance breakers (ARBs). Indeed, bacterial efflux pumps have a key role in AMR development; for instance, NorA efflux pump contributes to Staphylococcus aureus (S. aureus) resistance against fluoroquinolone antibiotics (e.g., ciprofloxacin) by promoting their active extrusion from the cells. Even though NorA efflux pump is known to be a potential target for EPIs development, the absence of structural information about this protein and the little knowledge available on its mechanism of action have strongly hampered rational drug discovery efforts in this area. In the present work, we investigated at the molecular level the substrate recognition pathway of NorA through a Supervised Molecular Dynamics (SuMD) approach, using a NorA homology model. Specific amino acids were identified as playing a key role in the efflux pump-mediated extrusion of its substrate, paving the way for a deeper understanding of both the mechanisms of action and the inhibition of such efflux pumps.

Revisiting the Allosteric Regulation of Sodium Cation on the Binding of Adenosine at the Human A2A Adenosine Receptor: Insights from Supervised Molecular Dynamics (SuMD) Simulations

One of the most intriguing findings highlighted from G protein-coupled receptor (GPCR) crystallography is the presence, in many members of class A, of a partially hydrated sodium ion in the middle of the seven transmembrane helices (7TM) bundle. In particular, the human adenosine A2A receptor (A2A AR) is the first GPCR in which a monovalent sodium ion was crystallized in a distal site from the canonical orthosteric one, corroborating, from a structural point of view, its role as a negative allosteric modulator. However, the molecular mechanism by which the sodium ion influences the recognition of the A2A AR agonists is not yet fully understood. In this study, the supervised molecular dynamics (SuMD) technique was exploited to analyse the sodium ion recognition mechanism and how its presence influences the binding of the endogenous agonist adenosine. Due to a higher degree of flexibility of the receptor extracellular (EC) vestibule, we propose the sodium-bound A2A AR as less efficient in stabilizing the adenosine during the different steps of binding.

Can We Still Trust Docking Results? An Extension of the Applicability of DockBench on PDBbind Database

The number of entries in the Protein Data Bank (PDB) has doubled in the last decade, and it has increased tenfold in the last twenty years. The availability of an ever-growing number of structures is having a huge impact on the Structure-Based Drug Discovery (SBDD), allowing investigation of new targets and giving the possibility to have multiple structures of the same macromolecule in a complex with different ligands. Such a large resource often implies the choice of the most suitable complex for molecular docking calculation, and this task is complicated by the plethora of possible posing and scoring function algorithms available, which may influence the quality of the outcomes. Here, we report a large benchmark performed on the PDBbind database containing more than four thousand entries and seventeen popular docking protocols. We found that, even in protein families wherein docking protocols generally showed acceptable results, certain ligand-protein complexes are poorly reproduced in the self-docking procedure. Such a trend in certain protein families is more pronounced, and this underlines the importance in identification of a suitable protein–ligand conformation coupled to a well-performing docking protocol.

Targeting Protein Kinase CK1δ with Riluzole: Could It Be One of the Possible Missing Bricks to Interpret Its Effect in the Treatment of ALS from a Molecular Point of View?

Riluzole, approved by the US Food and Drug Administration (FDA) in 1995, is the most widespread oral treatment for the fatal neurodegenerative disorder amyotrophic lateral sclerosis (ALS). The drug, whose mechanism of action is still obscure, mitigates progression of the illness, but unfortunately with only limited improvements. Herein we report the first demonstration, using a combination of computational and in vitro studies, that riluzole is an ATP-competitive inhibitor of the protein kinase CK1 isoform δ, with an IC₅₀ value of 16.1 μm. This allows us to rewrite its possible molecular mechanism of action in the treatment of ALS. The inhibition of CK1δ catalytic activity indeed links the two main pathological hallmarks of ALS: transactive response DNA-binding protein of 43 kDa (TDP-43) proteinopathy and glutamate excitotoxicity, exacerbated by the loss of expression of glial excitatory amino acid transporter-2 (EAAT2).