Computational discovery and experimental verification of tyrosine kinase inhibitor pazopanib for the reversal of memory and cognitive deficits in rat model neurodegeneration

The interference between SARS-COV-2 and Alzheimer's disease: Potential immunological and neurobiological crosstalk from a kinase perspective reveals a delayed pandemic

Coronavirus disease 2019 (COVID-19) has infected over 700 million people, with up to 30% developing neurological manifestations, including dementias. However, there is a lack of understanding of common molecular brain markers causing Alzheimer's disease (AD). COVID-19 has etiological cofactors with AD, making patients with AD a vulnerable population at high risk of experiencing more severe symptoms and worse consequences. Both AD and COVID-19 have upregulated several shared kinases, leading to the repositioning of kinase inhibitors (KIs) for the treatment of both diseases. This review provides an overview of the interactions between the immune system and the nervous system in relation to receptor tyrosine kinases, including epidermal growth factor receptors, vascular growth factor receptors, and non-receptor tyrosine kinases such as Bruton tyrosine kinase, spleen tyrosine kinase, c-ABL, and JAK/STAT. We will discuss the promising results of kinase inhibitors in pre-clinical and clinical studies for both COVID-19 and Alzheimer's disease (AD), as well as the challenges in repositioning KIs for these diseases. Understanding the shared kinases between AD and COVID-19 could help in developing therapeutic approaches for both.

Cognitive enhancing effects of pazopanib in D‑galactose/ovariectomized Alzheimer's rat model: insights into the role of RIPK1/RIPK3/MLKL necroptosis signaling pathway

Necroptosis, a programmed form of necrotic cell death carried out by receptor-interacting serine/threonine protein kinase 1 (RIPK1) and RIPK3, has been found to be implicated in the pathogenesis of Alzheimer's disease (AD). An FDA-approved anti-cancer drug, pazopanib, is reported to possess potent inhibitory effect against necroptosis via interfering with RIPK1. So far, there are no existing data on the influence of pazopanib on necroptotic pathway in AD. Thus, this study was designed to explore the impact of pazopanib on cognitive impairment provoked by ovariectomy (OVX) together with D-galactose (D-Gal) administration in rats and to scrutinize the putative signaling pathways underlying pazopanib-induced effects. Animals were allocated into four groups; the first and second groups were exposed to sham operation and administered normal saline and pazopanib (5 mg/kg/day, i.p.), respectively, for 6 weeks, while the third and fourth groups underwent OVX then were injected with D-Gal (150 mg/kg/day, i.p.); concomitantly with pazopanib in the fourth group for 6 weeks. Pazopanib ameliorated cognitive deficits as manifested by improved performance in the Morris water maze besides reversing the histological abnormalities. Pazopanib produced a significant decline in p-Tau and amyloid beta (Aβ) plaques. The neuroprotective effect of pazopanib was revealed by hampering neuroinflammation, mitigating neuronal death and suppressing RIPK1/RIPK3/MLKL necroptosis signaling pathway. Accordingly, hindering neuroinflammation and the necroptotic RIPK1/RIPK3/MLKL pathway could contribute to the neuroprotective effect of pazopanib in D-Gal/OVX rat model. Therefore, this study reveals pazopanib as a valuable therapeutic agent in AD that warrants future inspection to provide further data regarding its neuroprotective effect.

FDA-Approved Kinase Inhibitors in Preclinical and Clinical Trials for Neurological Disorders

Cancers and neurological disorders are two major types of diseases. We previously developed a new concept termed "Aberrant Cell Cycle Diseases" (ACCD), revealing that these two diseases share a common mechanism of aberrant cell cycle re-entry. The aberrant cell cycle re-entry is manifested as kinase/oncogene activation and tumor suppressor inactivation, which are hallmarks of both tumor growth in cancers and neuronal death in neurological disorders. Therefore, some cancer therapies (e.g., kinase inhibition, tumor suppressor elevation) can be leveraged for neurological treatments. The United States Food and Drug Administration (US FDA) has so far approved 74 kinase inhibitors, with numerous other kinase inhibitors in clinical trials, mostly for the treatment of cancers. In contrast, there are dire unmet needs of FDA-approved drugs for neurological treatments, such as Alzheimer's disease (AD), intracerebral hemorrhage (ICH), ischemic stroke (IS), traumatic brain injury (TBI), and others. In this review, we list these 74 FDA-approved kinase-targeted drugs and identify those that have been reported in preclinical and/or clinical trials for neurological disorders, with a purpose of discussing the feasibility and applicability of leveraging these cancer drugs (FDA-approved kinase inhibitors) for neurological treatments.

Estimating the Similarity between Protein Pockets

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.

Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems

We give a review on the landscape theory of the equilibrium biological systems and landscape-flux theory of the nonequilibrium biological systems as the global driving force. The emergences of the behaviors, the associated thermodynamics in terms of the entropy and free energy and dynamics in terms of the rate and paths have been quantitatively demonstrated. The hierarchical organization structures have been discussed. The biological applications ranging from protein folding, biomolecular recognition, specificity, biomolecular evolution and design for equilibrium systems as well as cell cycle, differentiation and development, cancer, neural networks and brain function, and evolution for nonequilibrium systems, cross-scale studies of genome structural dynamics and experimental quantifications/verifications of the landscape and flux are illustrated. Together, this gives an overall global physical and quantitative picture in terms of the landscape and flux for the behaviors, dynamics and functions of biological systems.

Binding Mechanism between Acetylcholinesterase and Drugs Pazopanib and Lapatinib: Biochemical and Biophysical Studies

Tyrosine kinase inhibitors (TKIs) are antitumor compounds that prevent the phosphorylation of proteins in a biological environment. However, the multitarget performance of TKIs promotes them as possible candidates for drug repositioning. In this work, interaction and inhibition studies through spectroscopic and computational techniques to evaluate the binding effectiveness of lapatinib and pazopanib TKIs to acetylcholinesterase (AChE) are reported. The results indicated potent inhibition at the μM level. The types of inhibition were identified, with pazopanib acting through non-competitive inhibition and lapatinib through acompetitive inhibition. The fluorescence suppression studies indicate a static mechanism for lapatinib-AChE and pazopanib-AChE systems, with a binding constant in the order of 10⁵ M^-1. The obtained thermodynamic parameters reveal interactions driven by van der Waals forces and hydrogen bonds in the lapatinib-AChE system (ΔH° and ΔS° < 0). In contrast, the pazopanib-AChE system shows positive ΔH° and ΔS°, characteristic of hydrophobic interactions. The Foster resonance energy transfer study supports the fluorescence studies performed. The 3D fluorescence studies suggest changes in the microenvironment of the tryptophan and tyrosine residues of the protein in contact with lapatinib and pazopanib. The results suggest effective inhibition and moderate interaction of the drugs with AChE, making them interesting for conducting more in-depth repositioning studies as AChE inhibitors.

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.

Mechanisms Underlying Vascular Endothelial Growth Factor Receptor Inhibition-Induced Hypertension: The HYPAZ Trial

[Figure: see text].

Molecular interaction of anti-cancer ligands with human brain acetylcholinesterase

There are a significant number of cases whereby cancer patients belonging to the old age group additionally suffer from cognition decline (a hallmark feature of Alzheimer's disease). Hence, it is understandable that it would be a boon if certain drug molecules could provide health benefits to a patient suffering from cancer as well as Alzheimer's disease. The objective of the work was to identify anticancer molecule(s) whose chemical-skeleton could be used as 'seed' for future design of dual-acting drugs against Alzheimer's disease and cancer. The study employed criterion-based search, docking, SWISS-ADME-profiling, ▵ASA-calculations, molecular-overlay and 'MoMA'-simulation to query possible binding of selected anticancer molecules with human brain acetylcholinesterase (AChE). Molecular interactions of all of the top ranking ligands were analyzed. 'BOILED-egg' model was employed to query brain-penetration of the ligands. A detailed molecular-simulation-analysis was performed. Snapshots of different stages of dynamic molecular interactions (selected from 254 pdb files) were captured by MoMA LigPath, a robotics inspired simulation algorithm. The study concluded that chemical skeletons of 'Niraparib' and 'Ponatinib' might be used as 'seed(s)' for design of such drugs. If successfully materialized in future, this approach could decrease the total number of daily pills that an old patient needs to take. Furthermore, novel anticancer drugs could be synthesized that do not inhibit AChE (e.g. by removal/modification of moieties that are crucial to binding of anticancer drug to AChE) even if those happen to be 'Blood Brain Barrier'-permeable. Alternatively, fresh AChE-inhibitors could be designed based on the scaffolds of the aforementioned anticancer drugs.Communicated by Ramaswamy H. Sarma.

Protective role of anticancer drugs in neurodegenerative disorders: A drug repurposing approach

The disease heterogeneity and little therapeutic progress in neurodegenerative diseases justify the need for novel and effective drug discovery approaches. Drug repurposing is an emerging approach that reinvigorates the classical drug discovery method by divulging new therapeutic uses of existing drugs. The common biological background and inverse tuning between cancer and neurodegeneration give weight to the conceptualization of repurposing of anticancer drugs as novel therapeutics. Many studies are available in the literature, which highlights the success story of anticancer drugs as repurposed therapeutics. Among them, kinase inhibitors, developed for various oncology indications evinced notable neuroprotective effects in neurodegenerative diseases. In this review, we shed light on the salient role of multiple protein kinases in neurodegenerative disorders. We also proposed a feasible explanation of the action of kinase inhibitors in neurodegenerative disorders with more attention towards neurodegenerative disorders. The problem of neurotoxicity associated with some anticancer drugs is also highlighted. Our review encourages further research to better encode the hidden potential of anticancer drugs with the aim of developing prospective repurposed drugs with no toxicity for neurodegenerative disorders.

Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking

Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.

Repurposing of antipsychotics perphenazine for the treatment of endometrial cancer

Endometrial cancer (EC) is one of the most common and fatal gynecological cancers worldwide, but there is no effective treatment for the EC patients of progesterone resistance. Repurposing of existing drugs is a good strategy to discover new candidate drugs. In this text, perphenazine (PPZ), approved for psychosis therapy, was identified as a potential agent for the treatment of both progesterone sensitive and resistant endometrial cancer for the first time. Specifically, perphenazine exhibited good cell proliferation inhibition in Ishikawa (ISK) and KLE cell lines according to the CCK-8 assay and colony formation assay. It also reduced the cell migration of ISK and KLE cell lines in the light of the transwell migration assay. Annexin-V/PI double staining assay suggested that perphenazine could effectively induce ISK and KLE cell apoptosis. Moreover, results of western blot assay indicated perphenazine obviously inhibited the phosphorylation of Akt. Delightedly, PPZ also could significantly attenuate xenograft tumor growth at both 3 mg/kg and 15 mg/kg in mice without influencing the body weights.

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge.

Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Pazopanib Reduces Phosphorylated Tau Levels and Alters Astrocytes in a Mouse Model of Tauopathy

Hyperphosphorylation and aggregation of tau protein is a critical factor in many neurodegenerative diseases. These diseases are increasing in prevalence, and there are currently no cures. Previous work from our group and others has shown that tyrosine kinase inhibitors (TKIs) can stimulate autophagy, decrease pathological proteins, and improve symptoms in models of neurodegeneration. Here we examined the role of pazopanib in mouse models that express either human mutant P301L tau (TauP301L) or triple mutant amyloid precursor protein (3x-AβPP). The TauP301L mouse expresses P301L tau under the control of a prion promoter in both neurons and astrocytes, reminiscent of some human tauopathies. Pazopanib crosses the blood-brain barrier with no detectable peripheral off-side effects, and decreases p-tau in TauP301L mice. Pazopanib reaches a brain concentration sufficient for inhibition of several tyrosine kinases, including vascular endothelial growth factor receptors (VEGFRs). Further, pazopanib does not affect microglia but reduces astrocyte levels toward nontransgenic controls in TauP301L mice. Pazopanib does not alter amyloid beta levels or astrocytes in 3x-AβPP mice but modulates a number of inflammatory markers (IP-10, MIP-1α, MIP-1β, and RANTES). These data suggest that pazopanib may be involved in p-tau clearance and modulation of astrocytic activity in models of tauopathies.

Sunitinib, a Clinically Used Anticancer Drug, Is a Potent AChE Inhibitor and Attenuates Cognitive Impairments in Mice

Sunitinib, a tyrosine kinase inhibitor, is clinically used for the treatment of cancer. In this study, we found for the first time that sunitinib inhibits acetylcholinesterase (AChE) at submicromolar concentrations in vitro. In addition, sunitinib dramatically decreased the hippocampal and cortical activity of AChE in a time-dependent manner in mice. Molecular docking analysis further demonstrates that sunitinib might interact with both the catalytic anion and peripheral anionic sites within AChE, which is in accordance with enzymatic activity results showing that sunitinib inhibits AChE in a mixed pattern. Most importantly, we evaluated the effects of sunitinib on scopolamine-induced cognitive impairments in mice by using novel object recognition and Morris water maze tests. Surprisingly, sunitinib could attenuate cognitive impairments to a similar extent as donepezil, a marketed AChE inhibitor used for the treatment of Alzheimer's disease. In summary, our results have shown that sunitinib could potently inhibit AChE and attenuate cognitive impairments in mice.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

Probing Origin of Binding Difference of inhibitors to MDM2 and MDMX by Polarizable Molecular Dynamics Simulation and QM/MM-GBSA Calculation

Binding abilities of current inhibitors to MDMX are weaker than to MDM2. Polarizable molecular dynamics simulations (MD) followed by Quantum mechanics/molecular mechanics generalized Born surface area (QM//MM-GBSA) calculations were performed to investigate the binding difference of inhibitors to MDM2 and MDMX. The predicted binding free energies not only agree well with the experimental results, but also show that the decrease in van der Walls interactions of inhibitors with MDMX relative to MDM2 is a main factor of weaker bindings of inhibitors to MDMX. The analyses of dihedral angles based on MD trajectories suggest that the closed conformation formed by the residues M53 and Y99 in MDMX leads to a potential steric clash with inhibitors and prevents inhibitors from arriving in the deep of MDMX binding cleft, which reduces the van der Waals contacts of inhibitors with M53, V92, P95 and L98. The calculated results using the residue-based free energy decomposition method further prove that the interaction strength of inhibitors with M53, V92, P95 and L98 from MDMX are obviously reduced compared to MDM2. We expect that this study can provide significant theoretical guidance for designs of potent dual inhibitors to block the p53-MDM2/MDMX interactions.