Qualitative prediction of blood-brain barrier permeability on a large and refined dataset

Improving glioma drug delivery: A multifaceted approach for glioma drug development

Glioma is one of the most common central nervous system (CNS) cancers that can be found within the brain and the spinal cord. One of the pressing issues plaguing the development of therapeutics for glioma originates from the selective and semipermeable CNS membranes: the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB). It is difficult to bypass these membranes and target the desired cancerous tissue because the purpose of the BBB and BSCB is to filter toxins and foreign material from invading CNS spaces. There are currently four varieties of Food and Drug Administration (FDA)-approved drug treatment for glioma; yet these therapies have limitations including, but not limited to, relatively low transmission through the BBB/BSCB, despite pharmacokinetic characteristics that allow them to cross the barriers. Steps must be taken to improve the development of novel and repurposed glioma treatments through the consideration of pharmacological profiles and innovative drug delivery techniques. This review addresses current FDA-approved glioma treatments' gaps, shortcomings, and challenges. We then outline how incorporating computational BBB/BSCB models and innovative drug delivery mechanisms will help motivate clinical advancements in glioma drug delivery. Ultimately, considering these attributes will improve the process of novel and repurposed drug development in glioma and the efficacy of glioma treatment.

Brain targeted lactoferrin coated lipid nanocapsules for the combined effects of apocynin and lavender essential oil in PTZ induced seizures

Apocynin (APO) is a plant derived antioxidant exerting specific NADPH oxidase inhibitory action substantiating its neuroprotective effects in various CNS disorders, including epilepsy. Due to rapid elimination and poor bioavailability, treatment with APO is challenging. Correspondingly, novel APO-loaded lipid nanocapsules (APO-LNC) were formulated and coated with lactoferrin (LF-APO-LNC) to improve br ain targetability and prolong residence time. Lavender oil (LAV) was incorporated into LNC as a bioactive ingredient to act synergistically with APO in alleviating pentylenetetrazol (PTZ)-induced seizures. The optimized LF-APO-LAV/LNC showed a particle size 59.7 ± 4.5 nm with narrow distribution and 6.07 ± 1.6mV zeta potential) with high entrapment efficiency 92 ± 2.4% and sustained release (35% in 72 h). Following subcutaneous administration, LF-APO-LAV/LNC brought about ⁓twofold increase in plasma AUC and MRT compared to APO. A Log BB value of 0.2 ± 0.14 at 90 min reflects increased brain accumulation. In a PTZ-induced seizures rat model, LF-APO-LAV/LNC showed a Modified Racine score of 0.67 ± 0.47 with a significant increase in seizures latency and decrease in duration. Moreover, oxidant/antioxidant capacity and inflammatory markers levels in brain tissue were significantly improved. Histopathological and immunohistochemical assessment of brain tissue sections further supported these findings. The results suggest APO/LAV combination in LF-coated LNC as a promising approach to counteract seizures.

Concurrent Optimizations of Efficacy and Blood-Brain Barrier Permeability in New Macrocyclic LRRK2 Inhibitors for Potential Parkinson's Disease Therapeutics

The elevated activity of leucine-rich repeat kinase 2 (LRRK2) is implicated in the pathogenesis of Parkinson's disease (PD). The quest for effective LRRK2 inhibitors has been impeded by the formidable challenge of crossing the blood-brain barrier (BBB). We leveraged structure-based de novo design and developed robust three-dimensional quantitative structure-activity relationship (3D-QSAR) models to predict BBB permeability, enhancing the likelihood of the inhibitor's brain accessibility. Our strategy involved the synthesis of macrocyclic molecules by linking the two terminal nitrogen atoms of HG-10-102-01 with an alkyl chain ranging from 2 to 4 units, laying the groundwork for innovative LRRK2 inhibitor designs. Through meticulous computational and synthetic optimization of both biochemical efficacy and BBB permeability, 9 out of 14 synthesized candidates demonstrated potent low-nanomolar inhibition and significant BBB penetration. Further assessments of in vitro and in vivo effectiveness, coupled with pharmacological profiling, highlighted 8 as the promising new lead compound for PD therapeutics.

Non-animal models for blood-brain barrier permeability evaluation of drug-like compounds

Diseases related to the central nervous system (CNS) are major health concerns and have serious social and economic impacts. Developing new drugs for CNS-related disorders presents a major challenge as it actively involves delivering drugs into the CNS. Therefore, it is imperative to develop in silico methodologies to reliably identify potential lead compounds that can penetrate the blood-brain barrier (BBB) and help to thoroughly understand the role of different physicochemical properties fundamental to the BBB permeation of molecules. In this study, we have analysed the chemical space of the CNS drugs and compared it to the non-CNS-approved drugs. Additionally, we have collected a feature selection dataset from Muehlbacher et al. (J Comput Aided Mol Des 25(12):1095-1106, 2011. 10.1007/s10822-011-9478-1) and an in-house dataset. This information was utilised to design a molecular fingerprint that was used to train machine learning (ML) models. The best-performing models reported in this study achieved accuracies of 0.997 and 0.98, sensitivities of 1.0 and 0.992, specificities of 0.971 and 0.962, MCCs of 0.984 and 0.958, and ROC-AUCs of 0.997 and 0.999 on an imbalanced and a balanced dataset, respectively. They demonstrated overall good accuracies and sensitivities in the blind validation dataset. The reported models can be applied for fast and early screening drug-like molecules with BBB potential. Furthermore, the bbbPythoN package can be used by the research community to both produce the BBB-specific molecular fingerprints and employ the models mentioned earlier for BBB-permeability prediction.

Astragaloside IV as a Memory-Enhancing Agent: In Silico Studies with In Vivo Analysis and Post Mortem ADME-Tox Profiling in Mice

Many people around the world suffer from neurodegenerative diseases associated with cognitive impairment. As life expectancy increases, this number is steadily rising. Therefore, it is extremely important to search for new treatment strategies and to discover new substances with potential neuroprotective and/or cognition-enhancing effects. This study focuses on investigating the potential of astragaloside IV (AIV), a triterpenoid saponin with proven acetylcholinesterase (AChE)-inhibiting activity naturally occurring in the root of Astragalus mongholicus, to attenuate memory impairment. Scopolamine (SCOP), an antagonist of muscarinic cholinergic receptors, and lipopolysaccharide (LPS), a trigger of neuroinflammation, were used to impair memory processes in the passive avoidance (PA) test in mice. This memory impairment in SCOP-treated mice was attenuated by prior intraperitoneal (ip) administration of AIV at a dose of 25 mg/kg. The attenuation of memory impairment by LPS was not observed. It can therefore be assumed that AIV does not reverse memory impairment by anti-inflammatory mechanisms, although this needs to be further verified. All doses of AIV tested did not affect baseline locomotor activity in mice. In the post mortem analysis by mass spectrometry of the body tissue of the mice, the highest content of AIV was found in the kidneys, then in the spleen and liver, and the lowest in the brain.

In Silico and In Vitro Analyses to Repurpose Quercetin as a Human Pancreatic α-Amylase Inhibitor

Human pancreatic α-amylase (HPA), situated at the apex of the starch digestion hierarchy, is an attractive therapeutic approach to precisely regulate blood glucose levels, thereby efficiently managing diabetes. Polyphenols offer a natural and multifaceted approach to moderate postprandial sugar spikes, with their slight modulation in carbohydrate digestion and potential secondary benefits, such as antioxidant and anti-inflammatory effects. Taking into consideration the unfavorable side effects of currently available commercial medications, we aimed to study a library of polyphenols attributed to their remarkable antidiabetic properties and screened the most potent HPA inhibitor via a comprehensive in silico study encompassing molecular docking, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) calculation, molecular dynamics (MD) simulation, density functional theory (DFT) study, and pharmacokinetic properties followed by an in vitro assay. Significant hydrogen bonding with the catalytic triad residues of HPA, prominent MM/GBSA binding energy of -27.03 kcal/mol, and the stable nature of the protein-ligand complex with regard to 100 ns MD simulation screened quercetin as the best HPA inhibitor. Additionally, quercetin showed strong reactivity in the substrate-binding pocket of HPA and exhibited favorable pharmacokinetic properties with a considerable inhibitory concentration (IC50) of 57.37 ± 0.9 μg/mL against α-amylase. This study holds prospects for HPA inhibition and suggests quercetin as an approach to therapy for diabetes; however, it is imperative to conduct further research.

Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors

Histone deacetylase 3 (HDAC3) is an epigenetic regulator that involves gene expression, apoptosis, and cell cycle progression, and the overexpression of HDAC3 is accountable for several cancers, neurodegeneracy, and many other diseases. Therefore, HDAC3 emerged as a promising drug target for the novel drug design. Here, we carried out the pharmacophore modeling using 50 benzamide-based HDAC3 selective inhibitors and utilized it for PHASE ligand screening to retrieve the hits with similar pharmacophore features. The dataset inhibitors of best hypotheses used to build the 3D QSAR model and the generated 3D QSAR model resulted in good PLS statistics with a regression coefficient (R2) of 0.89, predictive coefficient (Q2) of 0.88, and Pearson-R factor of 0.94 indicating its excellent predictive ability. The hits retrieved from pharmacophore-based virtual screening were subjected to docking against HDAC3 for the identification of potential inhibitors. A total of 10 hitsM1 to M10 were ranked using their scoring functions and further subject to lead optimization. The Prime MM/GBSA, AutoDock binding free energies, and ADMET studies were implemented for the selection of lead candidates. The four ligand molecules M1, M2, M3, and M4 were identified as potential leads against HDAC3 after lead optimization. The top two leads M1 and M2 were subjected to MD simulations for their stability evaluation with HDAC3. The newly designed leads M11 and M12 were identified as HDAC3 potential inhibitors from MD simulations studies. Therefore, the outcomes of the present study could provide insights into the discovery of new potential HDAC3 inhibitors with improved selectivity and activity against a variety of cancers and neurodegenerative diseases.

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds

MOTIVATION

Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate.

RESULTS

Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates.

AVAILABILITY AND IMPLEMENTATION

Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

Synthesis and anti-cancer potential of potent peripheral MAOA inhibitors designed to limit blood:brain penetration

Monoamine oxidases (MAOA/MAOB) are enzymes known for their role in neurotransmitter regulation in the central nervous system (CNS). Irreversible and non-selective MAO inhibitors (MAOi's) were the first class of antidepressants, thus subsequent work on drugs such as the selective MAOA inhibitor clorgyline has focussed on selectivity and increased CNS penetration. MAOA is highly expressed in high grade and metastatic prostate cancer with a proposed effect on prostate cancer growth, recurrence, and drug resistance. A Phase II Clinical Trial has demonstrated the therapeutic effects of the irreversible nonselective MAOi phenelzine for prostate cancer. However, neurologic adverse effects led to early withdrawal in 25% of the enrolled patient-population. In this work, we revised the clorgyline scaffold with the goal of decreasing CNS penetration to minimize CNS-related side effects while retaining or enhancing MAOA inhibition potency and selectivity. Using the known co-crystal structure of clorgyline bound with FAD co-factor in the hMAOA active site as a reference, we designed and synthesized a series of compounds predicted to have lower CNS penetration (logBB). All synthesized derivatives displayed favorable drug-like characteristics such as predicted Caco-2 permeability and human oral absorption, and exhibited highly selective hMAOA binding interactions. Introduction of an HBD group (NH2 or OH) at position 5 of the phenyl ring clorgyline resulted in 3x more potent hMAOA inhibition with equivalent or better hMAOB selectivity, and similar prostate cancer cell cytotoxicity. In contrast, introduction of larger substituents at this position or at the terminal amine significantly reduced the hMAOA inhibition potency, attributed in part to a steric clash within the binding pocket of the MAOA active site. Replacement of the N-methyl group by a more polar, but larger 2-hydroxyethyl group did not enhance potency. However, introduction of a polar 2-hydroxy in the propyl chain retained the highly selective MAOA inhibition and cancer cell cytotoxicity of clorgyline while reducing its CNS score from 2 to 0. We believe that these results identify a new class of peripherally directed MAOIs that may allow safer therapeutic targeting of MAOA for a variety of anti-cancer and anti-inflammatory indications.

High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge

Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity end points include the no-observed-adverse-effect level (NOAEL) and estimated uncertainty for a particular category of effects. We have previously developed a new paradigm for estimating NOAELs based on chemoinformatics analysis and experimental study qualities from selected analogues, not relying on quantitative structure-activity relationships (QSARs) or rule-based SAR systems, which are not well-suited to end points for which the underpinning data are weakly grounded in specific chemical-biological interactions. The central hypothesis of this approach is that similar compounds have similar toxicity profiles and, hence, similar NOAEL values. Analogue quality (AQ) quantifies the suitability of an analogue candidate for reading across to the target by considering similarity from structure, physicochemical, ADME (absorption, distribution, metabolism, excretion), and biological perspectives. Biological similarity is based on experimental data; assay vectors derived from aggregations of ToxCast/Tox21 data are used to derive machine learning (ML) hybrid rules that serve as biological fingerprints to capture target-analogue similarity relevant to specific effects of interest, for example, hormone receptors (ER/AR/THR). Once one or more analogues have been qualified for read-across, a decision theory approach is used to estimate confidence bounds for the NOAEL of the target. The confidence interval is dramatically narrowed when analogues are constrained to biologically related profiles. Although this read-across process works well for a single target with several analogues, it can become unmanageable when, for example, screening multiple targets (e.g., virtual screening library) or handling a parent compound having numerous metabolites. To this end, we have established a digitalized framework to enable the assessment of a large number of substances, while still allowing for human decisions for filtering and prioritization. This workflow was developed and validated through a use case of a large set of bisphenols and their metabolites.

Explaining Blood-Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms

The blood-brain barrier (BBB) represents a major obstacle to delivering drugs to the central nervous system (CNS), resulting in the lack of effective treatment for many CNS diseases including brain cancer. To accelerate CNS drug development, computational prediction models could save the time and effort needed for experimental evaluation. Here, we studied BBB permeability focusing on active transport (influx and efflux) as well as passive diffusion using previously published and self-curated data sets. We created prediction models based on physicochemical properties, molecular substructures, or their combination to understand which mechanisms contribute to BBB permeability. Our results show that features that predicted passive diffusion over membranes overlap with features that explain endothelial permeation of approved CNS-active drugs. We also identified physical properties and molecular substructures that positively or negatively predicted BBB transport. These findings provide guidance toward identifying BBB-permeable compounds by optimally matching physicochemical and molecular properties to BBB transport mechanisms.

Exhaustive in silico design and screening of novel antipsychotic compounds with improved pharmacodynamics and blood-brain barrier permeation properties

Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics) and drug-receptor pharmacodynamics. Therefore, in this study, we developed and implemented the in silico pipeline to design novel compounds (n = 260) as leads using the standard drug scaffolds with improved PK/PD properties from the standard scaffolds. As a result, the best candidates (n = 3) were evaluated in molecular docking to interact with serotonin and dopamine receptors. Finally, haloperidol (HAL) derivative (1-(4-fluorophenyl)-4-(4-hydroxy-4-{4-[(2-phenyl-1,3-thiazol-4-yl)methyl]phenyl}piperidin-1-yl)butan-1-one) was identified as a "magic shotgun" lead compound with better affinity to the 5-HT2A, 5-HT1D, D2, D3, and 5-HT1B receptors than the control molecule. Additionally, this hit substance was predicted to possess similar BBB permeation properties and much lower toxicological profiles in comparison to HAL. Overall, the proposed rational drug design platform for novel antipsychotic drugs based on the BBB permeation and receptor binding might be an invaluable asset for a medicinal chemist or translational pharmacologist.Communicated by Ramaswamy H. Sarma.

Design, synthesis and biological evaluations of quinolone amides against African trypanosomiasis with improved solubility

The human African trypanosomiasis is a devastating parasitic infection, which is caused by the protozoan Trypanosoma brucei and transmitted by the bite of the tsetse fly. An untreated infection usually results in death and only few drugs with significant drawbacks are currently available for treatment. Previous investigations revealed the quinolone amide MB007 as a lead compound with an excellent selectivity for T. b. brucei. Here, new quinolone amides were synthesized for deeper insights into the structure-activity relationship. Furthermore, the aqueous solubility of the compounds was analyzed, as the poor solubility of previous quinolone amides impeded in vivo studies for target identification. The biological evaluation led to the new lead structure 9f, which exhibits a promising in vitro activity against T. b. brucei (IC50 = 22 nM) and showed no cytotoxicity against macrophages. Moreover, compounds 10b and 10c were discovered, which possessed an improved solubility combined with a decent selectivity.

Integrated approach for studying bioactive compounds from Cladosporium spp. against estrogen receptor alpha as breast cancer drug target

Cladosporium spp. have been reported for their great diversity of secondary metabolites which represent as a prominent base material for verifying the biological activities. Several bioactive compounds which have antimicrobial, cytotoxic, quorum sensing inhibitory and phytotoxic activities have been isolated from Cladosporium species. Most of them are still needed to be explored for their anticancer properties. Therefore, the present study is focused on screening and identifying the bioactive compounds of Cladosporium spp. for their anticancer activity via the integrated approaches of Molecular Docking (MD), Molecular Dynamics Simulation (MDS) and Density Functional Theory (DFT) studies. A total of 123 bioactive compounds of Cladosporium spp. were explored for their binding affinity with the selected breast cancer drug target receptor such as estrogen receptor alpha (PDB:6CBZ). The Molecular Docking studies revealed that amongst the bioactive compounds screened, Altertoxin X and Cladosporol H showed a good binding affinity of - 10.5 kcal/mol and - 10.3 kcal/mol, respectively, with the estrogen receptor alpha when compared to the reference compound (17[Formula: see text]-Estradiol: - 10.2 kcal/mol). The MDS study indicated the stable binding patterns and conformation of the estrogen receptor alpha-Altertoxin X complex in a stimulating environment. In addition, in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) study suggested that Altertoxin X has a good oral bioavailability with a high LD[Formula: see text] value of 2.375 mol/kg and did not cause any hepatotoxicity and skin sensitization. In summary, the integrated approaches revealed that Altertoxin X possesses a promising anticancer activity and could serve as a new therapeutic drug for breast cancer treatment.

In Vitro and In Silico Studies for the Identification of Potent Metabolites of Some High-Altitude Medicinal Plants from Nepal Inhibiting SARS-CoV-2 Spike Protein

Despite ongoing vaccination programs against COVID-19 around the world, cases of infection are still rising with new variants. This infers that an effective antiviral drug against COVID-19 is crucial along with vaccinations to decrease cases. A potential target of such antivirals could be the membrane components of the causative pathogen, SARS-CoV-2, for instance spike (S) protein. In our research, we have deployed in vitro screening of crude extracts of seven ethnomedicinal plants against the spike receptor-binding domain (S1-RBD) of SARS-CoV-2 using an enzyme-linked immunosorbent assay (ELISA). Following encouraging in vitro results for Tinospora cordifolia, in silico studies were conducted for the 14 reported antiviral secondary metabolites isolated from T. cordifolia-a species widely cultivated and used as an antiviral drug in the Himalayan country of Nepal-using Genetic Optimization for Ligand Docking (GOLD), Molecular Operating Environment (MOE), and BIOVIA Discovery Studio. The molecular docking and binding energy study revealed that cordifolioside-A had a higher binding affinity and was the most effective in binding to the competitive site of the spike protein. Molecular dynamics (MD) simulation studies using GROMACS 5.4.1 further assayed the interaction between the potent compound and binding sites of the spike protein. It revealed that cordifolioside-A demonstrated better binding affinity and stability, and resulted in a conformational change in S1-RBD, hence hindering the activities of the protein. In addition, ADMET analysis of the secondary metabolites from T. cordifolia revealed promising pharmacokinetic properties. Our study thus recommends that certain secondary metabolites of T. cordifolia are possible medicinal candidates against SARS-CoV-2.

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington's Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80-83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70-72% in negative predictivity, and 78-86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

New Myrtenal–Adamantane Conjugates Alleviate Alzheimer’s-Type Dementia in Rat Model

Alzheimer’s disease (AD) is a neurodegenerative disease associated with memory impairment and other central nervous system (CNS) symptoms. Two myrtenal–adamantane conjugates (MACs) showed excellent CNS potential against Alzheimer’s models. Adamantane is a common pharmacophore for drug design, and myrtenal (M) demonstrated neuroprotective effects in our previous studies. The aim of this study is to evaluate the MACs’ neuroprotective properties in dementia. Methods: Scopolamine (Scop) was applied intraperitoneally in Wistar rats for 11 days, simultaneously with MACs or M as a referent, respectively. Brain acetylcholine esterase (AChE) activity, noradrenaline and serotonin levels, and oxidative brain status determination followed behavioral tests on memory abilities. Molecular descriptors and docking analyses for AChE activity center affinity were performed. Results: M derivatives have favorable physicochemical parameters to enter the CNS. Both MACs restored memory damaged by Scop, showing significant AChE-inhibitory activity in the cortex, in contrast to M, supported by the modeling analysis. Moderate antioxidant properties were manifested by glutathione elevation and catalase activity modulation. MACs also altered noradrenaline and serotonin content in the hippocampus. Conclusion: For the first time, neuroprotective properties of two MACs in a rat dementia model were observed. They were stronger than the natural M effects, which makes the substances promising candidates for AD treatment.

Brain physiome: A concept bridging in vitro 3D brain models and in silico models for predicting drug toxicity in the brain

In the last few decades, adverse reactions to pharmaceuticals have been evaluated using 2D in vitro models and animal models. However, with increasing computational power, and as the key drivers of cellular behavior have been identified, in silico models have emerged. These models are time-efficient and cost-effective, but the prediction of adverse reactions to unknown drugs using these models requires relevant experimental input. Accordingly, the physiome concept has emerged to bridge experimental datasets with in silico models. The brain physiome describes the systemic interactions of its components, which are organized into a multilevel hierarchy. Because of the limitations in obtaining experimental data corresponding to each physiome component from 2D in vitro models and animal models, 3D in vitro brain models, including brain organoids and brain-on-a-chip, have been developed. In this review, we present the concept of the brain physiome and its hierarchical organization, including cell- and tissue-level organizations. We also summarize recently developed 3D in vitro brain models and link them with the elements of the brain physiome as a guideline for dataset collection. The connection between in vitro 3D brain models and in silico modeling will lead to the establishment of cost-effective and time-efficient in silico models for the prediction of the safety of unknown drugs.

In Silico Approaches for Addressing Challenges in CNS Radiopharmaceutical Design

Positron emission tomography (PET) is a highly sensitive and versatile molecular imaging modality that leverages radiolabeled molecules, known as radiotracers, to interrogate biochemical processes such as metabolism, enzymatic activity, and receptor expression. The ability to probe specific molecular and cellular events longitudinally in a noninvasive manner makes PET imaging a particularly powerful technique for studying the central nervous system (CNS) in both health and disease. Unfortunately, developing and translating a single CNS PET tracer for clinical use is typically an extremely resource-intensive endeavor, often requiring synthesis and evaluation of numerous candidate molecules. While existing in vitro methods are beginning to address the challenge of derisking molecules prior to costly in vivo PET studies, most require a significant investment of resources and possess substantial limitations. In the context of CNS drug development, significant time and resources have been invested into the development and optimization of computational methods, particularly involving machine learning, to streamline the design of better CNS therapeutics. However, analogous efforts developed and validated for CNS radiotracer design are conspicuously limited. In this Perspective, we overview the requirements and challenges of CNS PET tracer design, survey the most promising computational methods for in silico CNS drug design, and bridge these two areas by discussing the potential applications and impact of computational design tools in CNS radiotracer design.

In Silico Prediction of Skin Permeability Using a Two-QSAR Approach

Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.

DeepBBBP: High accuracy Blood-Brain-Barrier Permeability Prediction with a Mixed Deep Learning Model

Blood-brain-barrier permeability (BBBP) is an important property that is used to establish the drug-likeness of a molecule, as it establishes whether the molecule can cross the BBB when desired. It also eliminates those molecules which are not supposed to cross the barrier, as doing so would lead to toxicity. BBBP can be measured in vivo, in vitro or in silico. With the advent and subsequent rise of in silico methods for virtual drug screening, quite a bit of work has been done to predict this feature using statistical machine learning (ML) and deep learning (DL) based methods. In this work a mixed DL-based model, consisting of a Multi-layer Perceptron (MLP) and Convolutional Neural Network layers, has been paired with Mol2vec. Mol2vec is a convenient and unsupervised machine learning technique which produces high-dimensional vector representations of molecules and its molecular substructures. These succinct vector representations are utilized as inputs to the mixed DL model that is used for BBBP predictions. Several well-known benchmarks incorporating BBBP data have been used for supervised training and prediction by our mixed DL model which demonstrates superior results when compared to existing ML and DL techniques used for predicting BBBP.

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.

Large-Scale Evaluation of Collision Cross Sections to Investigate Blood-Brain Barrier Permeation of Drugs

Successful drug administration to the central nervous system requires accurate adjustment of the drugs’ molecular properties. Therefore, structure-derived descriptors of potential brain therapeutic agents are essential for an early evaluation of pharmacokinetics during drug development. The collision cross section (CCS) of molecules was recently introduced as a novel measurable parameter to describe blood-brain barrier (BBB) permeation. This descriptor combines molecular information about mass, structure, volume, branching and flexibility. As these chemical properties are known to influence cerebral pharmacokinetics, CCS determination of new drug candidates may provide important additional spatial information to support existing models of BBB penetration of drugs. Besides measuring CCS, calculation is also possible; but however, the reliability of computed CCS values for an evaluation of BBB permeation has not yet been fully investigated. In this work, prediction tools based on machine learning were used to compute CCS values of a large number of compounds listed in drug libraries as negative or positive with respect to brain penetration (BBB⁺ and BBB⁻ compounds). Statistical evaluation of computed CCS and several other descriptors could prove the high value of CCS. Further, CCS-deduced maximum molecular size of BBB⁺ drugs matched the dimensions of BBB pores. A threshold for transcellular penetration and possible permeation through pore-like openings of cellular tight-junctions is suggested. In sum, CCS evaluation with modern in silico tools shows high potential for its use in the drug development process.

A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB-) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and https://doi.org/10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB- compounds.

Recent Progress on Biological Activity of Amaryllidaceae and Further Isoquinoline Alkaloids in Connection with Alzheimer's Disease

Alzheimer’s disease (AD) is a progressive age-related neurodegenerative disease recognized as the most common form of dementia among elderly people. Due to the fact that the exact pathogenesis of AD still remains to be fully elucidated, the treatment is only symptomatic and available drugs are not able to modify AD progression. Considering the increase in life expectancy worldwide, AD rates are predicted to increase enormously, and thus the search for new AD drugs is urgently needed. Due to their complex nitrogen-containing structures, alkaloids are considered to be promising candidates for use in the treatment of AD. Since the introduction of galanthamine as an antidementia drug in 2001, Amaryllidaceae alkaloids (AAs) and further isoquinoline alkaloids (IAs) have been one of the most studied groups of alkaloids. In the last few years, several compounds of new structure types have been isolated and evaluated for their biological activity connected with AD. The present review aims to comprehensively summarize recent progress on AAs and IAs since 2010 up to June 2021 as potential drugs for the treatment of AD.

Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods

The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sensitivity (SEN). To improve performance, ensemble models were built to predict the BBB permeability of compounds. In this study, in silico ensemble-learning models were developed using 3 machine-learning algorithms and 9 molecular fingerprints from 1757 chemicals (integrated from 2 published data sets) to predict BBB permeability. The best prediction performance of the base classifier models was achieved by a prediction model based on an random forest (RF) and a MACCS molecular fingerprint with an ACC of 0.910, an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.957, a SEN of 0.927, and a specificity of 0.867 in 5-fold cross-validation. The prediction performance of the ensemble models is better than that of most of the base classifiers. The final ensemble model has also demonstrated good accuracy for an external validation and can be used for the early screening of CNS drugs.

Structure Elucidation and Cholinesterase Inhibition Activity of Two New Minor Amaryllidaceae Alkaloids

Two new minor Amaryllidaceae alkaloids were isolated from Hippeastrum × hybridum cv. Ferrari and Narcissus pseudonarcissus cv. Carlton. The chemical structures were identified by various spectroscopic (one- and two-dimensional (1D and 2D) NMR, circular dichroism (CD), high-resolution mass spectrometry (HRMS) and by comparison with literature data of similar compounds. Both isolated alkaloids were screened for their human acetylcholinesterase (hAChE) and butyrylcholinesterase (hBuChE) inhibition activity. One of the new compounds, a heterodimer alkaloid of narcikachnine-type, named narciabduliine (2), showed balanced inhibition potency for both studied enzymes, with IC₅₀ values of 3.29 ± 0.73 µM for hAChE and 3.44 ± 0.02 µM for hBuChE. The accommodation of 2 into the active sites of respective enzymes was predicted using molecular modeling simulation.

Benzothiazolyl and Benzoxazolyl Hydrazones Function as Zinc Metallochaperones to Reactivate Mutant p53

We identified a set of thiosemicarbazone (TSC) metal ion chelators that reactivate specific zinc-deficient p53 mutants using a mechanism called zinc metallochaperones (ZMCs) that restore zinc binding by shuttling zinc into cells. We defined biophysical and cellular assays necessary for structure-activity relationship studies using this mechanism. We investigated an alternative class of zinc scaffolds that differ from TSCs by substitution of the thiocarbamoyl moiety with benzothiazolyl, benzoxazolyl, and benzimidazolyl hydrazones. Members of this series bound zinc with similar affinity and functioned to reactivate mutant p53 comparable to the TSCs. Acute toxicity and efficacy assays in rodents demonstrated C1 to be significantly less toxic than the TSCs while demonstrating equivalent growth inhibition. We identified C85 as a ZMC with diminished copper binding that functions as a chemotherapy and radiation sensitizer. We conclude that the benzothiazolyl, benzoxazolyl, and benzimidazolyl hydrazones can function as ZMCs to reactivate mutant p53 in vitro and in vivo.

Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

In silico Exploration of Bioactive Phytochemicals Against Neurodegenerative Diseases Via Inhibition of Cholinesterases

Neurodegenerative disorders are estimated to become the second leading cause of death worldwide by 2040. Despite the widespread use of diverse allopathic drugs, these brain-associated disorders can only be partially addressed and long term treatment is often linked with dependency and other unwanted side effects. Nature, believed to be an arsenal of remedies for any illness, presents an interesting avenue for the development of novel neuroprotective agents. Interestingly, inhibition of cholinesterases, involved in the breakdown of acetylcholine in the synaptic cleft, has been proposed to be neuroprotective. This review therefore aims to provide additional insight via docking studies of previously studied compounds that have shown potent activity against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) in vitro. Indeed, the determination of potent plant-based ligands for this purpose through in silico methods enables the elimination of lengthy and costly traditional methods of drug discovery. Herein, a literature search was conducted to identify active phytochemicals which are cholinesterase inhibitors. Following which in silico docking methods were applied to obtain docking scores. Compound structures were extracted from online ZINC database and optimized using AM1 implemented in gaussian09 software. Noteworthy ligands against AChE highlighted in this study include: 19,20-dihydroervahanine A and 19, 20-dihydrotabernamine. Regarding BChE inhibition, the best ligands were found to be 8-Clavandurylkaempferol, Na-methylepipachysamine D; ebeiedinone; and dictyophlebine. Thus, ligand optimization between such phytochemicals and cholinesterases coupled with in vitro, in vivo studies and randomized clinical trials can lead to the development of novel drugs against neurodegenerative disorders.

Potential natural inhibitors of xanthine oxidase and HMG-CoA reductase in cholesterol regulation: in silico analysis

Background

Hypercholesterolemia has posed a serious threat of heart diseases and stroke worldwide. Xanthine oxidase (XO), the rate-limiting enzyme in uric acid biosynthesis, is regarded as the root of reactive oxygen species (ROS) that generate atherosclerosis and cholesterol crystals. β-Hydroxy β-methylglutaryl-coenzyme A reductase (HMGR) is a rate-limiting enzyme in cholesterol biosynthesis. Although some commercially available enzyme inhibiting drugs have effectively reduced cholesterol levels, most of them have failed to meet potential drug candidates’ requirements. Here, we have carried out an in-silico analysis of secondary metabolites that have already shown good inhibitory activity against XO and HMGR in a wet lab setup.

Methods

Out of 118 secondary metabolites reviewed, sixteen molecules inhibiting XO and HMGR were selected based on the IC₅₀ values reported in in vitro assays. Further, receptor-based virtual screening was carried out against secondary metabolites using GOLD Protein-Ligand Docking Software, combined with subsequent post-docking, to study the binding affinities of ligands to the enzymes. In-silico ADMET analysis was carried out to explore their pharmacokinetic properties, followed by toxicity prediction through ProTox-II.

Results

The molecular docking of amentoflavone (GOLD score 70.54, ∆G _calc. = − 10.4 Kcal/mol) and ganomycin I (GOLD score 59.61, ∆G _calc. = − 6.8 Kcal/mol) displayed that the drug has effectively bound at the competitive site of XO and HMGR, respectively. Besides, 6-paradol and selgin could be potential drug candidates inhibiting XO. Likewise, n-octadecanyl-O-α-D-glucopyranosyl (6′ → 1″)-O-α-D-glucopyranoside could be potential drug candidates to maintain serum cholesterol. In-silico ADMET analysis has shown that these sixteen metabolites were optimal within the categorical range compared to commercially available XO and HMGR inhibitors, respectively. Toxicity analysis through ProTox-II revealed that 6-gingerol, ganoleucoin K, and ganoleucoin Z are toxic for human use.

Conclusion

This computational analysis supports earlier experimental evidence towards the inhibition of XO and HMGR by natural products. Further study is necessary to explore the clinical efficacy of these secondary molecules, which might be alternatives for the treatment of hypercholesterolemia.

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.

A Recurrent Neural Network model to predict blood-brain barrier permeability

The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as "chemoinformatics," which is a discipline that uses machine-learning techniques to extract, process, and extrapolate data from chemical structures. One of the significant lines of research in chemoinformatics is the study of blood-brain barrier (BBB) permeability, which aims to identify drug penetration into the central nervous system (CNS). In this research, we attempt to solve the problem of BBB permeability by predicting compounds penetration to the CNS. To accomplish this goal: (i) First, an overview is provided to the field of chemoinformatics, its definition, applications, and challenges, (ii) Second, a broad view is taken to investigate previous machine-learning and deep-learning computational models to solve BBB permeability. Based on the analysis of previous models, three main challenges that collectively affect the classifier performance are identified, which we define as "the triple constraints"; subsequently, we map each constraint to a proposed solution, (iii) Finally, we conclude this endeavor by proposing a deep learning based Recurrent Neural Network model, to predict BBB permeability (RNN-BBB model). Our model outperformed other studies from the literature by scoring an overall accuracy of 96.53%, and a specificity score of 98.08%. The obtained results confirm that addressing the triple constraints substantially improves the classification model capability specifically when predicting compounds with low penetration.

In vivo and in silico characterization of apocynin in reducing organ oxidative stress: A pharmacokinetic and pharmacodynamic study

Apocynin has been widely used in vivo as a Nox2-contaninig nicotinamide adenine dinucleotide phosphate oxidase inhibitor. However, its time-dependent tissue distribution and inhibition on organ reactive oxygen species (ROS) production remained unclear. In this study, we examined apocynin pharmacokinetics and pharmacodynamics (PKPD) after intravenous (iv) injection (bolus, 5 mg/kg) of mice (CD1, 12-week). Apocynin was detected using a HPLC coupled to a linear ion-trap tandem mass spectrometer. Apocynin peak concentrations were detected in plasma at 1 minute (5494 ± 400 ng/mL) (t1/2  = 0.05 hours, clearance = 7.76 L/h/kg), in urine at 15 minutes (14 942 ± 5977 ng/mL), in liver at 5 minutes (2853 ± 35 ng/g), in heart at 5 minutes (3161 ± 309 ng/g) and in brain at 1 minute (4603 ± 208 ng/g) after iv injection. These were accompanied with reduction of ROS production in the liver, heart and brain homogenates. Diapocynin was not detected in these samples. Therapeutic effect of apocynin was examined using a mouse model (C57BL/6J) of high-fat diet (HFD, 16 weeks)-induced obesity and accelerated aging. Apocynin (5 mmol/L) was supplied in drinking water during the HFD period and was detected at the end of treatment in the brain (5369 ± 1612 ng/g), liver (4818 ± 1340 ng/g) and heart (1795 ± 1487 ng/g) along with significant reductions of ROS production in these organs. In conclusion, apocynin PKPD is characterized by a short half-life, rapid clearance, good distribution and inhibition of ROS production in major organs. Diapocynin is not a metabolite of apocynin in vivo. Apocynin crosses easily the blood-brain barrier and reduces brain oxidative stress associated with metabolic disorders and aging.

Aromatic Esters of the Crinane Amaryllidaceae Alkaloid Ambelline as Selective Inhibitors of Butyrylcholinesterase

A total of 20 derivatives (1-20) of the crinane-type alkaloid ambelline were synthesized. These semisynthetic derivatives were assessed for their potency to inhibit both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). To predict central nervous system (CNS) availability, logBB was calculated, and the data correlated well with those obtained from the parallel artificial membrane permeability assay (PAMPA). All compounds should be able to permeate the blood-brain barrier (BBB) according to the obtained results. A total of 7 aromatic derivatives (5, 6, 7, 9, 10, 12, and 16) with different substitution patterns showed inhibitory potency against human serum BuChE (IC₅₀ < 5 μM), highlighting the three top-ranked compounds as follows: 11-O-(1-naphthoyl)ambelline (16), 11-O-(2-methylbenzoyl)ambelline (6), and 11-O-(2-methoxybenzoyl)ambelline (9) with IC₅₀ values of 0.10 ± 0.01, 0.28 ± 0.02, and 0.43 ± 0.04 μM, respectively. Notably, derivatives 6, 7, 9, and 16 displayed selective human BuChE (hBuChE) inhibition profiles with a selectivity index > 100. The in vitro results were supported by computational studies predicting plausible binding modes of the compounds in the active sites of hBuChE.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r² = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q² = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q² = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

Functionalized aromatic esters of the Amaryllidaceae alkaloid haemanthamine and their in vitro and in silico biological activity connected to Alzheimer's disease

A novel series of aromatic esters (1a-1m) related to the Amaryllidaceae alkaloid (AA) haemanthamine were designed, synthesized and tested in vitro with particular emphasis on the treatment of neurodegenerative diseases. Some of the synthesized compounds revealed promising acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitory profile. Significant human AChE (hAChE) inhibition was demonstrated by 11-O-(3-nitrobenzoyl)haemanthamine (1j) with IC₅₀value of 4.0 ± 0.3 µM. The strongest human BuChE (hBuChE) inhibition generated 1-O-(2-methoxybenzoyl)haemanthamine (1g) with IC₅₀ value 3.3 ± 0.4 µM. Moreover, 11-O-(2-chlorbenzoyl)haemanthamine (1m) was able to inhibit both enzymes in dose-dependent manner. The mode of hAChE and hBuChE inhibition was minutely inspected using enzyme kinetic analysis in tandem with in silico experiments, the latter elucidating crucial interaction in 1j-, 1m-hAChE and 1g-, 1m-hBuChE complexes. The blood-brain barrier (BBB) permeability was investigated applying the parallel artificial membrane permeation assay (PAMPA) to predict the CNS availability of the compounds.

A general linear free energy relationship for predicting partition coefficients of neutral organic compounds

Predicting the effects of organic compounds on environments and biological systems is an important objective for environmental chemistry and human health. The logarithm (to base 10) of the n-octanoll-water partition coefficient has been widely used to predict the mentioned properties. However, the suitability of this parameter for the purpose has been questioned, since the environments relating to the properties may be quite different from that of bulk n-octanol. In this study, we used a theoretical derivation approach to develop a model for predicting the partition coefficients of solutes between water and an organic solvent that may be similar to n-octanol or quite different from it. Our model relies on solute descriptors that can be calculated based on solute structures. It was used to predict the n-octanoll-water, hexadecanel-water and chloroforml-water partition coefficients of solutes. The calculated values of the examined parameters agreed well with their experimental counterparts. The model can find application in the accurate prediction of the effects of organic compounds on environments and the physicochemical properties of organic compounds by a full in-silico approach and can provide useful guidance for improving some properties of organic compounds.

Stephalagine, an aporphine alkaloid from Annona crassiflora fruit peel, induces antinociceptive effects by TRPA1 and TRPV1 channels modulation in mice

Pain relief represents a critical unresolved medical need. Consequently, the search for new analgesic agents is intensively studied. Annona crassiflora, a native species of the Brazilian Savanna, represents a potential source for painful treatment. This study aimed to investigate the antinociceptive potential of A. crassiflora fruit peel, focusing on its major alkaloid, stephalagine, in animal models of pain evoked by the activation of transient receptor potential ankyrin 1 (TRPA1) and vanilloid 1 (TRPV1) channels. Male C57BL/6/J mice were submitted to formalin-, cinnamaldehyde-, and capsaicin-induced nociception tests to assess nociceptive behavior, and to the open-field and rotarod tests for motor performance analyses. Moreover, the stephalagine's effect was tested on capsaicin- and cinnamaldehyde-induced Ca²⁺ influx in spinal cord synaptosomes. In silico assessments of the absorption, distribution, metabolism and central nervous system permeability of stephalagine were carried out. The ethanol extract and alkaloidal fraction reduced the nociception induced by formalin. When administered by oral route (1 mg/kg), stephalagine reduced the spontaneous nociception and paw edema induced by TRPV1 agonist, capsaicin, and by TRPA1 agonists, cinnamaldehyde- and formalin, without altering the animals' locomotor activity. The prediction of in silico pharmacokinetic properties of stephalagine suggests its capacity to cross the blood-brain barrier. Furthermore, this alkaloid reduces the capsaicin- and cinnamaldehyde-mediated Ca²⁺ influx, indicating a possible modulation of TRPV1 and TRPA1 channels, respectively. Together, our results support the antinociceptive and anti-edematogenic effects of the A. crassiflora fruit peel and suggest that these effects are triggered, at least in part, by TRPV1 and TRPA1 modulation by stephalagine.

Tuning Properties for Blood-Brain Barrier Permeation: A Statistics-Based Analysis

In the effort to define a set of rules useful in tuning the properties for a successful blood-brain barrier (BBB) permeation, we statistically analyzed a set of 328 compounds and correlated their experimental in vivo logBB with a series of computed descriptors. Contingency tables were constructed, observed and expected distributions were calculated, and chi-square (χ²) distributions were evaluated. This allowed to point out a significant dependence of certain physicochemical properties in influencing the BBB permeation. Of over 15 computed descriptors, 9 resulted to be particularly important showing highly significant χ² distribution: polar surface area (χ² = 66.79; p = 1.08 × 10^-13), nitrogen and oxygen count (χ² = 51.17; p = 2.06 × 10^-10), logP (χ² = 47.38; p = 1.27 × 10^-9), nitrogen count (χ² = 38.29; p = 9.77 × 10^-8), logD (χ² = 36.80; p = 36.80), oxygen count (χ² = 35.83; p = 3.13 × 10^-7), ionization state (χ² = 33.02, p = 3.19 × 10^-7), hydrogen bond acceptors (χ² = 30.80; p = 3.36 × 10^-6), and hydrogen bond donors (χ² = 29.29; p = 6.81 × 10^-6). Other parameters describing the mass and size of the molecules (molecular weight: 11.18; p = 2.46 × 10^-2) resulted in being not significant since the population within the observed and expected distribution was similar. Depending on the combination of the significant descriptors, we set a three cases probabilistic scenario (BBB⁺, BBB^-, BBB⁺/BBB^-) that would prospectively be used to tune properties for BBB permeation.

In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

In Vitro and In Silico Acetylcholinesterase Inhibitory Activity of Thalictricavine and Canadine and Their Predicted Penetration across the Blood-Brain Barrier

In recent studies, several alkaloids acting as cholinesterase inhibitors were isolated from Corydalis cava (Papaveraceae). Inhibitory activities of (+)-thalictricavine (1) and (+)-canadine (2) on human acetylcholinesterase (hAChE) and butyrylcholinesterase (hBChE) were evaluated with the Ellman's spectrophotometric method. Molecular modeling was used to inspect the binding mode of compounds into the active site pocket of hAChE. The possible permeability of 1 and 2 through the blood⁻brain barrier (BBB) was predicted by the parallel artificial permeation assay (PAMPA) and logBB calculation. In vitro, 1 and 2 were found to be selective hAChE inhibitors with IC50 values of 0.38 ± 0.05 µM and 0.70 ± 0.07 µM, respectively, but against hBChE were considered inactive (IC50 values > 100 µM). Furthermore, both alkaloids demonstrated a competitive-type pattern of hAChE inhibition and bind, most probably, in the same AChE sub-site as its substrate. In silico docking experiments allowed us to confirm their binding poses into the active center of hAChE. Based on the PAMPA and logBB calculation, 2 is potentially centrally active, but for 1 BBB crossing is limited. In conclusion, 1 and 2 appear as potential lead compounds for the treatment of Alzheimer's disease.

Derivatives of the β-Crinane Amaryllidaceae Alkaloid Haemanthamine as Multi-Target Directed Ligands for Alzheimer's Disease

Twelve derivatives 1a–1m of the β-crinane-type alkaloid haemanthamine were developed. All the semisynthetic derivatives were studied for their inhibitory potential against both acetylcholinesterase and butyrylcholinesterase. In addition, glycogen synthase kinase 3β (GSK-3β) inhibition potency was evaluated in the active derivatives. In order to reveal the availability of the drugs to the CNS, we elucidated the potential of selected derivatives to penetrate through the blood-brain barrier (BBB). Two compounds, namely 11-O-(2-methylbenzoyl)-haemanthamine (1j) and 11-O-(4-nitrobenzoyl)-haemanthamine (1m), revealed the most intriguing profile, both being acetylcholinesterase (hAChE) inhibitors on a micromolar scale, with GSK-3β inhibition properties, and predicted permeation through the BBB. In vitro data were further corroborated by detailed inspection of the compounds’ plausible binding modes in the active sites of hAChE and hBuChE, which led us to provide the structural determinants responsible for the activity towards these enzymes.

Identification of dual Sigma1 receptor modulators/acetylcholinesterase inhibitors with antioxidant and neurotrophic properties, as neuroprotective agents

In this manuscript we report on the design, synthesis and evaluation of dual Sigma 1 Receptor (S1R) modulators/Acetylcholinesterase (AChE) inhibitors endowed with antioxidant and neurotrophic properties, potentially able to counteract neurodegeneration. The compounds based on arylalkylaminoketone scaffold integrate the pharmacophoric elements of RRC-33, a S1R modulator developed by us, donepezil, a well-known AChE inhibitor, and curcumin, a natural antioxidant compound with neuroprotective properties. A small library of compounds was synthesized and preliminary in vitro screening performed. Some compounds showed good S1R binding affinity, selectivity towards S2R and N-Methyl-d-Aspartate (NMDA) receptor, AChE relevant inhibiting activity and are potentially able to bypass the BBB, as predicted by the in silico study. For the hits 10 and 20, the antioxidant profile was assessed in SH-SY5Y human neuroblastoma cell lines by evaluating their protective effect against H2O2 cytotoxicity and reactive oxygen species (ROS) production. Tested compounds resulted effective in decreasing ROS production, thus ameliorating the cellular survival. Moreover, compounds 10 and 20 showed to be effective in promoting the neurite elongation of Dorsal Root Ganglia (DRG), thus demonstrating a promising neurotrophic activity. Of note, the tested compounds did not show any cytotoxic effect at the concentration assayed. Relying on these encouraging results, both compounds will undergo a structure optimization program for the development of therapeutic candidates for neurodegenerative diseases treatment.

In Silico Prediction of Blood-Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all models were built using imbalanced data sets, which caused a high false-positive rate. Therefore, we tried to solve the problem of biased data sets and built a reliable classification model with 2358 compounds. Machine learning and resampling methods were used simultaneously for the refinement of models with both 2 D molecular descriptors and molecular fingerprints to represent the chemicals. Through a series of evaluation, we realized that resampling methods such as Synthetic Minority Oversampling Technique (SMOTE) and SMOTE+edited nearest neighbor could effectively solve the problem of imbalanced data sets and that MACCS fingerprint combined with support vector machine performed the best. After the final construction of a consensus model, the overall accuracy rate was increased to 0.966 for the final external data set. Also, the accuracy rate of the model for the test set was 0.919, with an excellent balanced capacity of 0.925 (sensitivity) to predict BBB-positive compounds and of 0.899 (specificity) to predict BBB-negative compounds. Compared with other BBB classification models, our models reduced the rate of false positives and were more robust in prediction of BBB-positive as well as BBB-negative compounds, which would be quite helpful in early drug discovery.