Spirocyclic β-site amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitors: from hit to lowering of cerebrospinal fluid (CSF) amyloid β in a higher species

Association of plasma zinc and copper levels with mild cognitive impairment in patients with type 2 diabetes

Background

Type 2 diabetes mellitus (T2DM) is a significant risk factor for cognitive impairment. Zinc deficiency contributes to T2DM development, while copper may exacerbate diabetes through prooxidant mechanisms. Higher zinc levels may protect against copper toxicity. This study investigates the association of plasma zinc and copper levels with mild cognitive impairment (MCI) in T2DM patients.

Methods

T2DM patients admitted to Tongji Hospital from 2012 to 2018 were classified into MCI (n = 136) and control (n = 136) groups, matched by age (± 3 years) and gender. Conditional logistic regression was used to assess the associations between plasma zinc, copper levels and MCI. A generalized additive model (GAM) evaluated the dose-response relationship between plasma zinc, copper levels and Mini-Mental State Examination (MMSE) scores.

Results

The median of plasma metal levels in MCI and control groups were 831.31 μg/L and 936.29 μg/L for zinc, 932.07 μg/L and 860.47 μg/L for copper, and 0.91 and 1.11 for the zinc-to-copper (Zn/Cu) ratio. Compared to participants in the lowest tertile, the multivariable-adjusted odds ratios with 95% confidence intervals (CI) for MCI in the highest tertile were 0.33 (0.13, 0.79) for zinc, 3.56 (1.42, 8.94) for copper, and 0.37 (0.15, 0.93) for the Zn/Cu ratio. Plasma Aβ40 levels were significantly lower (p = 0.009) and plasma Aβ42/40 levels were significantly higher (p = 0.008) in MCI group compared with those in control group. Zinc concentration was positively associated with Aβ42. For per SD (327.71 μg/L) increase in plasma zinc levels, the percent change (95% CI) of Aβ42 were 2.90 (0.85, 4.99).

Conclusion

Higher plasma zinc levels and higher Zn/Cu ratio were associated with lower odds of MCI in T2DM patients, while higher copper levels increased the risk of MCI. This study provides insights on plasma zinc, copper, and Zn/Cu ratio and Aβ of MCI, further studies are needed to clarify the underlying mechanisms for novel therapies that could prevent or cure multiple T2DM-related cognitive impairments.

Multi-modal neuroprotection of Argemone mexicana L. against Alzheimer's disease: In vitro and in silico study

Argemone mexicana L. is a medicinal plant, but its impact on Alzheimer's disease (AD) is right now undetermined. We intended to investigate the in-vitro anti-AD potential of leaves and flowers of A. mexicana methanol, ethanol, and ethyl extracts and to identify multi-modal anti-AD phytochemicals by computational approaches. Molecular docking of 196 phytochemicals identified three hit phytochemicals (protoberberine, protopine, and codeine) with higher binding affinity and multi-targeting ability toward AChE, BChE, BACE-1, and GSK-3β. Further MM-GBSA assays confirmed the integrity of these phytochemicals as the hit phytochemicals. However, these phytochemicals demonstrated favorable pharmacokinetics (PK) and drugable properties having no toxicity. Molecular dynamics simulations confirmed the binding strength of the hit phytoconstituents in the active pockets of AChE, BChE, BACE-1, and GSK-3β with multi-targeting inhibitory activities. All the extracts exhibited dose-dependent antioxidant and anti-cholinesterase activities supporting the in silico results in the context of oxidative stress and cholinergic pathways. Our results offer scientific validation of the anti-AD properties of Argemone mexicana L. and identified protoberberine, protopine, and codeine that could be used for the development of multi-modal inhibitors of AChE, BChE, BACE-1, and GSK-3β to combat AD. Additional in vivo validation is recommended to ensure a thorough assessment in the present research.

Current State of Open Source Force Fields in Protein-Ligand Binding Affinity Predictions

In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.

Flavonoid and Chalcone Scaffolds as Inhibitors of BACE1: Recent Updates

Flavonoids and chalcones are two major classes of chemical moieties that have a vast background of pharmacological activities. Chalcone is a subclass of flavonoids whose therapeutic potential has been implicated due to an array of bioactivities. A lot of research works have shown interest in investigating the neuroprotective effect of these molecules, and have revealed them to be much more potent molecules that can be used to treat neurodegenerative disorders. Beta-site APP cleaving enzyme (BACE1), which is majorly found in the brain, is one of the reasons behind the development of Alzheimer's disease (AD). Flavonoids and chalcones have proven clinical data that they inhibit the production of Aβ plaques that are involved in the progression of AD. In this article, we have provided a detailed chronological review of the research work on the BACE1 inhibiting potency of both flavonoids and chalcones. Almost all the flavonoids and chalcones mentioned in this article have shown very good in vitro and in vivo BACE1 inhibiting activity. The docking studies and the structural importance of some BACE1-inhibiting flavonoids, as well as chalcones, are also mentioned here.

Broadening the Scope of Binding Free Energy Calculations Using a Separated Topologies Approach

Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.

An open-source molecular builder and free energy preparation workflow

Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

The accurate and reliable prediction of protein-ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling). Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behavior. We find that the "enhanced sampling" method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure, and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

Research Advancements on Fluorinated and Non-Fluorinated 4-Phenyl(thio)ureido-Substituted 2,2-Dimethylchromans Acting as Inhibitors of Insulin Release and Smooth Muscle Relaxants

AIMS

The present study aimed at characterizing the impact of the presence or absence of fluorine atoms on the phenyl and benzopyran rings of 4-phenyl(thio)ureido-substituted 2,2-dimethylchromans on their ability to inhibit insulin release from pancreatic -cells or to relax vascular smooth muscle cells.

METHODS

Most compounds were found to inhibit insulin secretion and to provoke a marked myorelaxant activity.

RESULTS

The lack of a fluorine or a chlorine atom at the 6-position of the 2,2-dimethylchroman core structure reduced the inhibitory activity on the pancreatic endocrine tissue. One of the most active compounds on both tissues, compound 11h (BPDZ 678), was selected for further pharmacological investigations.

CONCLUSION

The biological data suggested that 11h mainly expressed the profile of a KATP channel opener on pancreatic -cells, although a calcium entry blockade effect was also observed. On vascular smooth muscle cells, 11h behaved as a calcium entry blocker.

Radical addition to the CC bond meets (1,n)-HAT: recent advances in the remote C(sp3)–H or C(sp2)–H functionalization of alkenes

This review summarizes the recent development of remote C(sp3)–H bond or aldehydic C(sp2)–H functionalizations enabled by intermolecular radical addition to CC bond/(1,n)-HAT tandem sequences.

Novel small molecule therapeutic agents for Alzheimer disease: Focusing on BACE1 and multi-target directed ligands

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that effects 50 million people worldwide. In this review, AD pathology and the development of novel therapeutic agents targeting AD were fully discussed. In particular, common approaches to prevent Aβ production and/or accumulation in the brain including α-secretase activators, specific γ-secretase modulators and small molecules BACE1 inhibitors were reviewed. Additionally, natural-origin bioactive compounds that provide AD therapeutic advances have been introduced. Considering AD is a multifactorial disease, the therapeutic potential of diverse multi target-directed ligands (MTDLs) that combine the efficacy of cholinesterase (ChE) inhibitors, MAO (monoamine oxidase) inhibitors, BACE1 inhibitors, phosphodiesterase 4D (PDE4D) inhibitors, for the treatment of AD are also reviewed. This article also highlights descriptions on the regulator of serotonin receptor (5-HT), metal chelators, anti-aggregants, antioxidants and neuroprotective agents targeting AD. Finally, current computational methods for evaluating the structure-activity relationships (SAR) and virtual screening (VS) of AD drugs are discussed and evaluated.

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.

Molecular Mechanism of Binding Selectivity of Inhibitors toward BACE1 and BACE2 Revealed by Multiple Short Molecular Dynamics Simulations and Free-Energy Predictions

The β-amyloid cleaving enzymes 1 and 2 (BACE1 and BACE2) have been regarded as the prospective targets for clinically treating Alzheimer's disease (AD) in the last two decades. Thus, insight into the binding differences of inhibitors to BACE1 and BACE2 is of significance for designing highly selective inhibitors toward the two proteins. In this work, multiple short molecular dynamics (MSMD) simulations are coupled with the molecular mechanics generalized Born surface area (MM-GBSA) method to probe the binding selectivity of three inhibitors DBO, CS9, and SC7 on BACE1 over BACE2. The results show that the entropy effect plays a key role in selectivity identification of inhibitors toward BACE1 and BACE2, which determines that DBO has better selectivity toward BACE2 over BACE1, while CS9 and CS7 can more favorably bind to BACE1 than BACE2. The hierarchical clustering analysis based on energetic contributions of residues suggests that BACE1 and BACE2 share the common hot interaction spots. The residue-based free-energy decomposition method was applied to compute the inhibitor-residue interaction spectrum, and the results recognize four common binding subpockets corresponding to the different groups of inhibitors, which can be used as efficient targets for designing highly selective inhibitors toward BACE1 and BACE2. Therefore, these results provide a useful molecular basis and dynamics information for development of highly selective inhibitors targeting BACE1 and BACE2.

Protein-Protein Interactions and Aggregation Inhibitors in Alzheimer's Disease

BACKGROUND

Alzheimer's Disease (AD), a multifaceted disorder, involves complex pathophysiology and plethora of protein-protein interactions. Thus such interactions can be exploited to develop anti-AD drugs.

OBJECTIVE

The interaction of dynamin-related protein 1, cellular prion protein, phosphoprotein phosphatase 2A and Mint 2 with amyloid β, etc., studied recently, may have critical role in progression of the disease. Our objective has been to review such studies and their implications in design and development of drugs against the Alzheimer's disease.

METHODS

Such studies have been reviewed and critically assessed.

RESULTS

Review has led to show how such studies are useful to develop anti-AD drugs.

CONCLUSION

There are several PPIs which are current topics of research including Drp1, Aβ interactions with various targets including PrPC, Fyn kinase, NMDAR and mGluR5 and interaction of Mint2 with PDZ domain, etc., and thus have potential role in neurodegeneration and AD. Finally, the multi-targeted approach in AD may be fruitful and opens a new vista for identification and targeting of PPIs in various cellular pathways to find a cure for the disease.

Association Between Axonopathy and Amyloid Plaques in the Spinal Cord of the Transgenic Mice of Alzheimer's Disease

Axonopathy manifested by axon swellings might constitute one of the earliest pathological features of Alzheimer's disease. It has been proposed that axonopathy might be associated with the origin of Aβ plaques. However, how axonopathy leads to Aβ plaque pathogenesis remains elusive. Our previous studies have shown that Aβ neuropathology (mainly diffuse plaques) selectively occurred in the regions of corticospinal tract (CST) pathway and its innervated region in the spinal cord of TgCRND8 mice. In this study, we investigated the occurrence and progression of axonopathy and the possible implication in Aβ plaque pathogenesis in the spinal cord of TgCRND8 mice. By anterograde labeling of CST system with a neuroanatomical tracer, we found that dilated corticospinal axons started to appear at 7 months, then exhibited an age-dependent increase. These abnormal structures appear before any plaque deposits are visible in the spinal cord of the mice. Importantly, they colocalized with Aβ plaques in either the white matter or gray matter of the spinal cord at later stages, suggesting that these axonal swellings might represent the initial stages of Aβ plaque formation, and could play a role in Aβ plaque pathogenesis. Furthermore, using ultrastructural analysis we demonstrated that intracellular contents in the axonal dystrophies such as various dense vesicles leaked out into the extracellular matrix under a condition of axon swelling rupture in CST pathways of spinal cord. This provided precise structural evidence that how the Aβ leaks out from the axonal dystrophies into extracellular matrix and how an axonal swelling might serve as a nidus of amyloid plaque formation.

Simultaneous Monitoring of Amyloid-β (Aβ) Oligomers and Fibrils for Effectively Evaluating the Dynamic Process of Aβ Aggregation

Herein, we provide a proof of concept for a novel strategy that targets the assessment of the aggregation of amyloid-β (Aβ) by simultaneously determining its oligomers (Aβo) and fibrils (Aβf) in one analytical system. By fabricating and combining two immunosensors for Aβo and Aβf, respectively, we constructed a two-channel electrochemical system. The ratio of Aβf to Aβo was calculated and taken as a possible criterion for evaluating the extent of aggregation. Thereby, the presence of and transformation between oligomers and fibrils were accurately probed by incubating the Aβ monomer for different times and then calculating the ratios of Aβf to Aβo. The applicability of this method was further validated by tracking the dynamic progress of Aβ aggregation in the cerebrospinal fluid and tissues of Alzheimer's disease (AD) rats, which revealed that the ratio of Aβf to Aβo in rat brain gradually increased with the progression of AD, which was indicative of the severity of peptide aggregation during this process. Overall, this study represents the first example of a quantitative strategy for precisely evaluating the aggregation process that is related to pathological events in AD brain.

BACE1 gene silencing alleviates isoflurane anesthesia‑induced postoperative cognitive dysfunction in immature rats by activating the PI3K/Akt signaling pathway

Postoperative cognitive dysfunction (POCD) is a severe complication characterized by cognitive dysfunction following anesthesia and surgery. The aim of the present study was to investigate the effects of β-site amyloid precursor protein cleavage enzyme 1 (BACE1) gene silencing on isoflurane anesthesia-induced POCD in immature rats via the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt) signaling pathway. Rat models were established and then transfected with BACE1 small interfering RNA and wortmannin (an inhibitor of PI3K). Blood gas analysis was performed, and a series of behavioral experiments were conducted to evaluate the cognitive function, learning ability and locomotor activity of rats. Reverse transcription quantitative polymerase chain reaction and western blot analysis were employed to determine the mRNA and protein expression of the associated genes. An ELISA was used to detect the inflammatory indicators and the content of amyloid precursor protein (APP) and amyloid-β (Aβ). Apoptosis of the hippocampal CA1 region was observed by terminal deoxynucleotidyl transferase dUTP nick-end labeling staining. Initially, it was revealed that the percentage of stagnation time in rats was increased by BACE1 gene silencing; the escape latency and swimming distance were markedly reduced from the 4th to the 6th day, the time the rats spent in first passing the target area was shortened, and the times of passing the target area were increased by BACE1 gene silencing, demonstrating that BACE1 gene silencing enhanced the spatial memory ability of rats. Additionally, it was determined that silencing BACE1 improved the pathological state induced by isoflurane anesthesia in immature rats, and attenuated the inflammatory response and the levels of APP and Aβ in hippocampal tissues. Furthermore, it was suggested that silencing BACE1 may have promoted the activation of the PI3K/Akt signaling pathway, thereby inhibiting the apoptosis of the hippocampal CA1 region. Taken together, these results indicated that BACE1 gene silencing may improve isoflurane anesthesia-induced POCD in immature rats by activating the PI3K/Akt signaling pathway and inhibiting the Aβ generated by APP.

Highlights in BACE1 Inhibitors for Alzheimer's Disease Treatment

Alzheimer's disease (AD) is a severe neurodegenerative disorder and the most common type of dementia in the elderly. The clinical symptoms of AD include a progressive loss of memory and impairment of cognitive functions interfering with daily life activities. The main neuropathological features consist in extracellular amyloid-β (Aβ) plaque deposition and intracellular Neurofibrillary tangles (NFTs) of hyperphosphorylated Tau. Understanding the pathophysiological mechanisms that underlie neurodegeneration in AD is essential for rational design of neuroprotective agents able to prevent disease progression. According to the "Amyloid Cascade Hypothesis" the critical molecular event in the pathogenesis of AD is the accumulation of Aβ neurotoxic oligomers. Since the proteolytic processing of Amyloid Precursor Protein (APP) by β-secretase (beta-site APP cleaving enzyme 1, BACE1) is the rate-limiting step in the production of Aβ, this enzyme is considered a major therapeutic target and BACE1 inhibitors have the potential to be disease-modifying drugs for AD treatment. Therefore, intensive efforts to discover and develop inhibitors that can reach the brain and effectively inhibit BACE1 have been pursued by several groups worldwide. The aim of this review is to highlight the progress in the discovery of potent and selective small molecule BACE1 inhibitors over the past decade.

Development of 2-aminooxazoline 3-azaxanthene β-amyloid cleaving enzyme (BACE) inhibitors with improved selectivity against Cathepsin D

As part of an ongoing effort at Amgen to develop a disease-modifying therapy for Alzheimer's disease, we have previously used the aminooxazoline xanthene (AOX) scaffold to generate potent and orally efficacious BACE1 inhibitors. While AOX-BACE1 inhibitors demonstrated acceptable cardiovascular safety margins, a retinal pathological finding in rat toxicological studies demanded further investigation. It has been widely postulated that such retinal toxicity might be related to off-target inhibition of Cathepsin D (CatD), a closely related aspartyl protease. We report the development of AOX-BACE1 inhibitors with improved selectivity against CatD by following a structure- and property-based approach. Our efforts culminated in the discovery of a picolinamide-substituted 3-aza-AOX-BACE1 inhibitor absent of retinal effects in an early screening rat toxicology study.

Application of Free Energy Perturbation for the Design of BACE1 Inhibitors

Novel spiroaminodihydropyrroles probing for optimized interactions at the P3 pocket of β-secretase 1 (BACE1) were designed with the use of free energy perturbation (FEP) calculations. The resulting molecules showed pIC50 potencies in enzymatic BACE1 inhibition assays ranging from approximately 5 to 7. Good correlation was observed between the predicted activity from the FEP calculations and experimental activity. Simulations run with a default 5 ns approach delivered a mean unsigned error (MUE) between prediction and experiment of 0.58 and 0.91 kcal/mol for retrospective and prospective applications, respectively. With longer simulations of 10 and 20 ns, the MUE was in both cases 0.57 kcal/mol for the retrospective application, and 0.69 and 0.59 kcal/mol for the prospective application. Other considerations that impact the quality of the calculations are discussed. This work provides an example of the value of FEP as a computational tool for drug discovery.

Synthesis of amino heterocycle aspartyl protease inhibitors

Synthetic strategies to access 2-amino heterocycle head groups that inhibit aspartyl proteases, are reviewed.

Intriguing possibilities and beneficial aspects of transporter-conscious drug design

It has been revealed that many types of drugs interact with transporter proteins within an organism. Transporter proteins absorb or excrete materials, including drugs and nutrients, across the cell membrane. Some hydrophobic drugs are excreted from the cell as xenobiotics by ATP-binding cassette (ABC) transporters. However, solute carrier (SLC) transporters are tissue-specifically expressed and have substrate specificities. Thus, transporter-conscious drug design is an excellent method of delivering drugs to pharmaceutical target organs and provides advantages in absorption, distribution, excretion, and toxicity of drugs (ADMET) due to transport systems. In fact, based on this strategy, the bioavailability of prodrugs designed as peptide transporter 1 (PEPT1) substrates was better than that of the corresponding parent compounds due to the transport system in the small intestine. Furthermore, in central nervous system (CNS) drug developing, drug delivery into brain across the blood-brain barrier (BBB) is a serious problem. However, this problem can be also solved by the use of the transport systems at the BBB. Therefore, transporter-consciously designed drugs not only may effectively elicit activity but also may control adverse side effects caused by off-targets and drug-drug interactions and, consequently, may show good performance in clinical trials. In this review, I introduce possibilities and advantages of transporter-conscious drug designs.

Function and toxicity of amyloid beta and recent therapeutic interventions targeting amyloid beta in Alzheimer's disease

Our Feature Article details the physiological role of amyloid beta (Aβ), elaborates its toxic effects and outlines therapeutic molecules designed in the last two years targeting different aspects of Aβ for preventing AD.

8-Tetrahydropyran-2-yl chromans: highly selective beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitors

A series of 2,3,4,4a,10,10a-hexahydropyrano[3,2-b]chromene analogs was developed that demonstrated high selectivity (>2000-fold) for BACE1 vs Cathepsin D (CatD). Three different Asp-binding moieties were examined: spirocyclic acyl guanidines, aminooxazolines, and aminothiazolines in order to modulate potency, selectivity, efflux, and permeability. Guided by structure based design, changes to P2' and P3 moieties were explored. A conformationally restricted P2' methyl group provided inhibitors with excellent cell potency (37-137 nM) and selectivity (435 to >2000-fold) for BACE1 vs CatD. These efforts lead to compound 59, which demonstrated a 69% reduction in rat CSF Aβ1-40 at 60 mg/kg (PO).

Rational use of plasma protein and tissue binding data in drug design

It is a commonly accepted assumption that only unbound drug molecules are available to interact with their targets. Therefore, one of the objectives in drug design is to optimize the compound structure to increase in vivo unbound drug concentration. In this review, theoretical analyses and experimental observations are presented to illustrate that low plasma protein binding does not necessarily lead to high in vivo unbound plasma concentration. Similarly, low brain tissue binding does not lead to high in vivo unbound brain tissue concentration. Instead, low intrinsic clearance leads to high in vivo unbound plasma concentration, and low efflux transport activity at the blood-brain barrier leads to high unbound brain concentration. Plasma protein and brain tissue binding are very important parameters in understanding pharmacokinetics, pharmacodynamics, and toxicities of drugs, but these parameters should not be targeted for optimization in drug design.

In silico design of BACE1 inhibitor for Alzheimer's disease by traditional Chinese medicine

The β-site APP cleaving enzyme 1 (BACE1) is an important target for causing Alzheimer's disease (AD), due to the brain deposition peptide amyloid beta (Aβ) require cleavages of amyloid precursor protein (APP) by BACE1 and γ-secretase, but treatments of AD still have side effect in recent therapy. This study utilizes the world largest traditional Chinese medicine (TCM) database and database screening to provide potential BACE1 inhibited compound. Molecular dynamics (MD) simulation was carried out to observe the dynamics structure after ligand binding. We found that Triptofordin B1 has less toxicity than pyrimidine analogue, which has more potent binding affinity with BACE1. For trajectory analysis, all conformations are tending to be stable during 5000 ps simulation time. In dynamic protein validation, the residues of binding region are still stable after MD simulation. For snapshot comparison, we found that Triptofordin B1 could reduce the binding cavity; the results reveal that Triptofordin B1 could bind to BACE1 and better than control, which could be used as potential lead drug to design novel BACE1 inhibitor for AD therapy.

Elucidating the mechanism of cytochrome P450-mediated pyrimidine ring conversion to pyrazole metabolites with the BACE1 inhibitor GNE-892 in rats

We investigated an uncommon biotransformation of pyrimidine during the metabolism of GNE-892 ((R)-2-amino-1,3',3'-trimethyl-7'-(pyrimidin-5-yl)-3',4'-dihydro-2'H-spiro[imidazole-4,1'-naphthalen]-5(1H)-one), a β-secretase 1 inhibitor. Three novel metabolites, formed by conversion of pyrimidine to pyrazole, were observed in the (14)C-radiolabeled mass balance study in rats. Their structures were characterized by high-resolution mass spectrometry and nuclear magnetic resonance. Although these metabolites accounted for <5% of the administered dose, their unique nature prompted us to conduct further investigations. The pyrazole-containing metabolites were formed in vitro with rat hepatocytes and liver microsomes, which supported that they were formed during hepatic metabolism. Further, their generation was inhibited by 1-aminobenzotriazole, indicating involvement of cytochrome P450s. Studies with rat recombinant enzymes identified that CYP2D2 generated the N-hydroxypyrazole metabolite from GNE-892. This biotransformation proceeded through multiple steps from the likely precursor, pyrimidine N-oxide. On the basis of these data, we propose a mechanism in which the pyrimidine is activated via N-oxidation, followed by a second oxidative process that opens the pyrimidine ring to form a formamide intermediate. After hydrolysis of the formamide, a carbon is lost as formic acid, together with ring closure to form the pyrazole ring. This article highlights a mechanistic approach for determining the biotransformation of the pyrimidine to a pyrazole for GNE-892.

Discovery of 7-tetrahydropyran-2-yl chromans: β-site amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitors that reduce amyloid β-protein (Aβ) in the central nervous system

In an attempt to increase selectivity vs Cathepsin D (CatD) in our BACE1 program, a series of 1,3,4,4a,10,10a-hexahydropyrano[4,3-b]chromene analogues was developed. Three different Asp-binding moieties were examined: spirocyclic acyl guanidines, aminooxazolines, and aminothiazolines in order to modulate potency, selectivity, efflux, and permeability. Using structure-based design, substitutions to improve binding to both the S3 and S2' sites of BACE1 were explored. An acyl guanidine moiety provided the most potent analogues. These compounds demonstrated 10-420 fold selectivity for BACE1 vs CatD, and were highly potent in a cell assay measuring Aβ1-40 production (5-99 nM). They also suffered from high efflux. Despite this undesirable property, two of the acyl guanidines achieved free brain concentrations (Cfree,brain) in a guinea pig PD model sufficient to cover their cell IC50s. Moreover, a significant reduction of Aβ1-40 in guinea pig, rat, and cyno CSF (58%, 53%, and 63%, respectively) was observed for compound 62.

Pharmacogenomics of Alzheimer's disease: novel therapeutic strategies for drug development

Alzheimer's disease (AD) is a major problem of health and disability, with a relevant economic impact on our society. Despite important advances in pathogenesis, diagnosis, and treatment, its primary causes still remain elusive, accurate biomarkers are not well characterized, and the available pharmacological treatments are not cost-effective. As a complex disorder, AD is a polygenic and multifactorial clinical entity in which hundreds of defective genes distributed across the human genome may contribute to its pathogenesis. Diverse environmental factors, cerebrovascular dysfunction, and epigenetic phenomena, together with structural and functional genomic dysfunctions, lead to amyloid deposition, neurofibrillary tangle formation, and premature neuronal death, the major neuropathological hallmarks of AD. Future perspectives for the global management of AD predict that genomics and proteomics may help in the search for reliable biomarkers. In practical terms, the therapeutic response to conventional drugs (cholinesterase inhibitors, multifactorial strategies) is genotype-specific. Genomic factors potentially involved in AD pharmacogenomics include at least five categories of gene clusters: (1) genes associated with disease pathogenesis; (2) genes associated with the mechanism of action of drugs; (3) genes associated with drug metabolism (phase I and II reactions); (4) genes associated with drug transporters; and (5) pleiotropic genes involved in multifaceted cascades and metabolic reactions. The implementation of pharmacogenomic strategies will contribute to optimize drug development and therapeutics in AD and related disorders.