Central Nervous System Drug Discovery Research Articles

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design.

Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

Abstract 7178: Neuronal cells derived from iPSCs cell to evaluate neurotoxicity after 48 or 72 hours in high-through put screening format

Abstract Neurotoxicity is a major concern in central nervous system drug discovery and a frequent cause of attrition in clinical trials or approved drug withdrawal. Neuropathy is a side effect defined by the injury of peripheral nerves causing a loss of sensation/motion. Drug development pipelines typically involve testing in cell lines followed by animal investigations with translation to humans. The induced pluripotent stem cells (iPSCs) have widened new model systems to study adverse toxicities. With evolution in regulatory guidance the use of human- iPSC-derived tissue provides high-throughput access and a more relevant testing environment. Neuropathy is one of the most chemotherapy adverse effects. In this study we have screened 8 compounds. Bortezomib (proteasome inhibitor), gemcitabine (approved ovarian cancer treatment), ivermectin (parasitic diseases treatment), flavopiridol (approved CDKs inhibitors), SNS-032 (approved CDKs inhibitor), mefloquine (anti-malaria drug), digitonin (nonionic detergent) and tamoxifen (hormone receptor-positive breast cancer treatment). These molecule toxicities were tested on neuronal-derived iPSCs Dopaneurone, GABAneurone, Glutaneurone, Motorneurone, Microglya and Astrocytes in high throughput screening. For the project, cells were plated in 384 well/plates. DMSO tolerance was performed (up to 10%). Drugs were added, then, after 48h or 72h, total protease and protease activities were measured using CytoTox-GloTM. Compounds were tested at 7 concentrations (from 10−4 to 10−9M).In this study we have validated cell culture and the assay conditions. The signal window range was good after 48h and 72h of treatment. No toxicity was found until 10% DMSO. A specific neurotoxicity signature was measurable for each drug tested. Digitonin, our positive control, induced toxicity in all IPSCs after 48h and 72h of incubation. On the contrary, Tamoxifen, our negative control, did not induce any neurotoxicity. The other molecules generated a signature of severe to mild neurotoxicity. These results are in agreement with their known adverse side-effects. In conclusion, this panel is a good tool to anticipate possible neurotoxicity within the 3Rs respect. It can be used in early drug de-risking or neuroprotection screening, with the aim of preventing/reducing/curing neuropathy in at-risk populations. Citation Format: Fabienne Sabatier, Justine Chaigne, Maryse Martin, Rodolphe Barbeau, Luisa Houssin, Stephane Bonnin, Alastair King, Vanessa Jahnke. Neuronal cells derived from iPSCs cell to evaluate neurotoxicity after 48 or 72 hours in high-through put screening format [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7178.

Fragment-based drug discovery for disorders of the central nervous system: designing better drugs piece by piece.

Fragment-based drug discovery (FBDD) has emerged as a powerful strategy to confront the challenges faced by conventional drug development approaches, particularly in the context of central nervous system (CNS) disorders. FBDD involves the screening of libraries that comprise thousands of small molecular fragments, each no greater than 300Da in size. Unlike the generally larger molecules from high-throughput screening that limit customisation, fragments offer a more strategic starting point. These fragments are inherently compact, providing a strong foundation with good binding affinity for the development of drug candidates. The minimal elaboration required to transition the hit into a drug-like molecule is not only accelerated, but also it allows for precise modifications to enhance both their activity and pharmacokinetic properties. This shift towards a fragment-centric approach has seen commercial success and holds considerable promise in the continued streamlining of the drug discovery and development process. In this review, we highlight how FBDD can be integrated into the CNS drug discovery process to enhance the exploration of a target. Furthermore, we provide recent examples where FBDD has been an integral component in CNS drug discovery programs, enabling the improvement of pharmacokinetic properties that have previously proven challenging. The FBDD optimisation process provides a systematic approach to explore this vast chemical space, facilitating the discovery and design of compounds piece by piece that are capable of modulating crucial CNS targets.

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

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. 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. 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.

A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, Kp,uu.

The blood-brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (Kp,uu). While various medicinal chemistry strategies and in silico models have been reported to improve BBB penetration, they have limited application in predicting Kp,uu directly. We describe a physics-based computational approach, a quantum mechanics (QM)-based energy of solvation (E-sol), to predict Kp,uu. Prospective application of this method in internal CNS drug discovery programs highlights the utility and accuracy of this new method, which showed a categorical accuracy of 79% and an R2 of 0.61 from a linear regression model.

Pharmacological modulation of chloride channels as a therapeutic strategy for neurological disorders.

Chloride homeostasis is critical in the physiological functions of the central nervous system (CNS). Its concentration is precisely regulated by multiple ion-transporting proteins such as chloride channels and transporters that are widely distributed in the brain cells, including neurons and glia. Unlike ion transporters, chloride channels provide rapid responses to efficiently regulate ion flux. Some of chloride channels are also permeable to selected organic anions such as glutamate and γ-aminobutyric acid, suggesting neuroexcitatory and neuroinhibitory functions while gating. Dysregulated chloride channels are implicated in neurological disorders, e.g., ischemia and neuroinflammation. Modulation of chloride homeostasis through chloride channels has been suggested as a potential therapeutic approach for neurological disorders. The drug design for CNS diseases is challenging because it requires the therapeutics to traverse the blood-brain-barrier. Small molecules are a well-established modality with better cell permeability due to their lower molecular weight and flexibility for structure optimization compared to biologics. In this article, we describe the important roles of chloride homeostasis in each type of brain cells and introduce selected chloride channels identified in the CNS. We then discuss the contribution of their dysregulations towards the pathogenesis of neurological disorders, emphasizing the potential of targeting chloride channels as a therapeutic strategy for CNS disease treatment. Along with this literature survey, we summarize the small molecules that modulate chloride channels and propose the potential strategy of optimizing existing drugs to brain-penetrants to support future CNS drug discovery.

Rapid progress in neuroimaging technologies fuels central nervous system translational medicine.

Central nervous system (CNS) drug discovery suffers from high attrition rates; translational neuroscience approaches aiming to reduce these high rates include the use of brain imaging technologies. However, there is a need to better understand what methods are being used and for what diseases and purposes. Our analysis of the literature found that magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) were the neuroimaging techniques used most often in clinical trials for the most prevalent CNS diseases: Alzheimer's disease (AD), Parkinson's disease (PD), depression, and schizophrenia. Moreover, the number of initiated clinical trials using MRI, PET, and SPECT increased over the period 1981-2021. Such insights indicate that the significant increase in the use of neuroimaging studies could decrease the attrition of novel drug candidates in late clinical development.

Recent advances in the in vitro and in vivo methods to assess impact of P-glycoprotein and breast cancer resistance protein transporters in central nervous system drug disposition.

One challenge in central nervous system (CNS) drug discovery has been ensuring the blood-brain barrier (BBB) penetration of compounds at an efficacious concentration that provides suitable safety margins for clinical investigation. Research providing for the accurate prediction of brain penetration of compounds during preclinical discovery is important to a CNS program. In the BBB, P-glycoprotein (P-gp) (ABCB1) and breast cancer resistance protein (BCRP) (ABCG2) transporters have been demonstrated to play a major role in the active efflux of endogenous compounds and xenobiotics out of the brain microvessel cells and back to the systemic circulation. In the past 10years, there has been significant technological improvement in the sensitivity of quantitative proteomics methods, in vivo imaging, in vitro methods of organoid and microphysiological systems, as well as in silico quantitative physiological based pharmacokinetic and systems pharmacology models. Scientists continually leverage these advancements to interrogate the distribution of compounds in the CNS which may also show signals of substrate specificity of P-gp and/or BCRP. These methods have shown promise toward predicting and quantifying the unbound concentration(s) within the brain relevant for efficacy or safety. In this review, the authors have summarized the in vivo, in vitro, and proteomics advancements toward understanding the contribution of P-gp and/or BCRP in restricting the entry of compounds to the CNS of either healthy or special populations. Special emphasis has been provided on recent investigations on the application of a proteomics-informed approach to predict steady-state drug concentrations in the brain. Moreover, future perspectives regarding the role of these transporters in newer modalities are discussed.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs.

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Anti-neuroinflammatory Effects and Brain Pharmacokinetic Properties of Selonsertib, an Apoptosis signal-regulating Kinase 1 Inhibitor, in mice.

Selonsertib is a first-in-class apoptosis signal-regulating kinase 1 (ASK1) inhibitor in clinical trials for treating NASH and diabetic kidney disease due to its anti-inflammatory and anti-fibrotic activities. In the present study, we investigated the anti-neuroinflammatory effects and brain pharmacokinetic properties of selonsertib. It inhibited inflammatory cytokines and NO production by suppressing phosphorylated ASK1 in the LPS-stimulated microglial cell line, BV2 cells. Consistent with the in vitro results, selonsertib attenuated plasma and brain TNF-α levels in the LPS-induced murine neuroinflammation model. In vitro and in vivo pharmacokinetic studies of selonsertib were conducted in support of central nervous system (CNS) drug discovery. In both Caco-2 and MDR-MDCK cells, selonsertib exhibited a high efflux ratio, showing that it is a P-gp substrate. Selonsertib was rapidly and effectively absorbed into the systemic circulation after oral treatment, with a Tmax of 0.5h and oral bioavailability of 74%. In comparison with high systemic exposure with Cmax of 16.2µg/ml and AUC of 64µg·h/mL following oral dosing of 10mg/kg, the brain disposition of selonsertib was limited, with Cmax of 0.08µg/g and Kp value of 0.004. This study demonstrates that selonsertib can be a therapeutic agent for neuroinflammatory diseases.

Recent Scaffold Hopping Applications in Central Nervous System Drug Discovery.

The concept of bioisosterism and the implementation of bioisosteric replacement is fundamental to medicinal chemistry. The exploration of bioisosteres is often used to probe key structural features of candidate pharmacophores and enhance pharmacokinetic properties. As the understanding of bioisosterism has evolved, capabilities to undertake more ambitious bioisosteric replacements have emerged. Scaffold hopping is a broadly used term in the literature referring to a variety of different bioisosteric replacement strategies, ranging from simple heterocyclic replacements to topological structural overhauls. In this work, we have highlighted recent applications of scaffold hopping in the central nervous system drug discovery space. While we have highlighted the benefits of using scaffold hopping approaches in central nervous system drug discovery, these are also widely applicable to other medicinal chemistry fields. We also recommend a shift toward the use of more refined and meaningful terminology within the realm of scaffold hopping.

Analysis of Antioxidant Constituents from Ilex rotunda and Evaluation of Their Blood-Brain Barrier Permeability.

Ilex rotunda Thunb., has been used to treat common cold, tonsillitis, and eczema. It is also a source of antioxidants. However, information regarding its antioxidative phytochemical composition is still incomplete and limited. In this present study, we initially determined DPPH radical scavenging activity of the extracts of I. rotunda fruits, twigs, and leaves. Among them, the twig extract exhibited a potential of antioxidant capacity. Based on antioxidant effect guided experiments, extraction condition using 80% EtOH was then optimized. DPPH and ABTS radical scavenging assays were also performed for fractions. The n-butanol fraction showed the highest antioxidant effect. Using chromatographic methods, eight marker compounds (1–8) were further isolated. Their structures were determined by spectroscopic and mass data. Method validation was employed to quantitate contents of these eight marker compounds. Subsequently, the HPLC-DPPH method was used to evaluate the contribution of certain compounds to total antioxidant activity of the extract. Lastly, parallel artificial membrane permeability assay for blood–brain barrier (PAMPA-BBB) was applied to investigate brain-penetrable antioxidants from I. rotunda extract. As a result, compound 7 (4,5-dicaffeoylquinic acid) showed significant antioxidant activity and penetration across the BBB via transcellular passive diffusion. Our findings suggested that compound 7 can be used as a therapeutic potential candidate in natural product-based central nervous system (CNS) drug discovery.

PET as a Translational Tool in Drug Development for Neuroscience Compounds.

In central nervous system drug discovery programs, early development of new chemical entities (NCEs) requires a multidisciplinary strategy and a translational approach to obtain proof of distribution, proof of occupancy, and proof of function in specific brain circuits. Positron emission tomography (PET) provides a way to assess in vivo the brain distribution of NCEs and their binding to the target of interest, provided that radiolabeling of the NCE is possible or that a suitable radioligand is available. PET is therefore a key tool for early phases of drug discovery programs. This review will summarize the main applications of PET in early drug development and discuss the usefulness of PET microdosing studies performed with direct labelling of the NCE and PET occupancy studies. The purpose of this review is also to propose an alignment of the nomenclatures used by drug metabolism and pharmacokinetic scientists and PET imaging scientists to indicate key pharmacokinetic parameters and to provide guidance in the performance and interpretation of PET studies.

In Vitro Models of the Blood-Brain Barrier.

Traditional in vitro models can replicate many essential features of drug transport/permeability across the blood-brain barrier (BBB) but are not entirely projecting in vivo central nervous system (CNS) uptake. Species differences fail to translate experimental therapeutics from the research laboratory to the clinic. Improved in vitro modeling of human BBB is vital for both CNS drug discovery and delivery. High-end human BBB models fabricated by microfluidic technologies offer some solutions to this problem. BBB's complex physiological microenvironment has been established by increasing device complexity in terms of multiple cells, dynamic conditions, and 3D designs. It is now possible to predict the therapeutic effects of a candidate drug and identify new druggable targets by studying multicellular interactions using the advanced in vitro BBB models. This chapter reviews the current as well as an ideal in vitro model of the BBB.

Resolving the age-related decline in central nervous system myelin turnover and drug discovery for oligodendroglial rejuvenation.

Resolving the age-related decline in central nervous system myelin turnover and drug discovery for oligodendroglial rejuvenation.

GSK-3β, FYN, and DYRK1A: Master Regulators in Neurodegenerative Pathways.

Protein kinases (PKs) have been recognized as central nervous system (CNS)-disease-relevant targets due to their master regulatory role in different signal transduction cascades in the neuroscience space. Among them, GSK-3β, FYN, and DYRK1A play a crucial role in the neurodegeneration context, and the deregulation of all three PKs has been linked to different CNS disorders with unmet medical needs, including Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal lobar degeneration (FTLD), and several neuromuscular disorders. The multifactorial nature of these diseases, along with the failure of many advanced CNS clinical trials, and the lengthy approval process of a novel CNS drug have strongly limited the CNS drug discovery. However, in the near-decade from 2010 to 2020, several computer-assisted drug design strategies have been combined with synthetic efforts to develop potent and selective GSK-3β, FYN, and DYRK1A inhibitors as disease-modifying agents. In this review, we described both structural and functional aspects of GSK-3β, FYN, and DYRK1A and their involvement and crosstalk in different CNS pathological signaling pathways. Moreover, we outlined attractive medicinal chemistry approaches including multi-target drug design strategies applied to overcome some limitations of known PKs inhibitors and discover improved modulators with suitable blood–brain barrier (BBB) permeability and drug-like properties.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Is PROTAC technology really a game changer for central nervous system drug discovery?

ABSTRACT Introduction: Central nervous system (CNS) diseases affect a large portion of the population, however, few therapeutic options are available. Furthermore, to date, clinical trials have been largely unsuccessful due to difficulty in targeting the undruggable, toxic proteins that underly many CNS disorders. PROteolysis Targeting Chimeras (PROTACs) are a rapidly emerging technology that has been proposed as a potential treatment option for various CNS diseases by hijacking the endogenous protein degradation process. Areas Covered: Herein, the authors discuss how the application of PROTACs may be beneficial in the treatment of major CNS diseases. They further discuss the main advantages and disadvantages of using PROTACs in the CNS, focusing on potential limitations such as their transient nature, localization, blood-brain barrier permeability and proteasome dysfunction. Expert opinion: It is evident that PROTACs have significant potential as a therapeutic tool for the treatment of CNS diseases and there is preliminary evidence suggesting that PROTACs could be successful in a clinical setting. Nevertheless, numerous limitations exist that must be overcome before this technology can be applied as a successful therapeutic for CNS disorders. Importantly, more in vivo studies are needed to determine the feasibility and effectiveness of using PROTACs in the brain.

Brain penetrating peptides and peptide-drug conjugates to overcome the blood-brain barrier and target CNS diseases.

Nearly one in six people worldwide suffer from disorders of the central nervous system (CNS). There is an urgent need for effective strategies to improve the success rates in CNS drug discovery and development. The lack of effective technologies for delivering drugs and genes to the brain due to the blood-brain barrier (BBB), a structural barrier that effectively blocks most neurotherapeutic agents from reaching the brain, has posed a formidable hurdle for CNS drug development. Brain-homing and brain-penetrating molecular transport vectors, such as brain permeable peptides or BBB shuttle peptides, have shown promise in overcoming the BBB and ferrying the drug molecules to the brain. The BBB shuttle peptides are discovered by phage display technology or derived from natural neurotropic proteins or certain viruses and harness the receptor-mediated transcytosis molecular machinery for crossing the BBB. Brain permeable peptide-drug conjugates (PDCs), composed of BBB shuttle peptides, linkers, and drug molecules, have emerged as a promising CNS drug delivery system by taking advantage of the endogenous transcytosis mechanism and tricking the brain into allowing these bioactive molecules to pass the BBB. Here, we examine the latest development of brain-penetrating peptide shuttles and brain-permeable PDCs as molecular vectors to deliver small molecule drug payloads across the BBB to reach brain parenchyma. Emerging knowledge of the contribution of the peptides and their specific receptors expressed on the brain endothelial cells, choice of drug payloads, the design of PDCs, brain entry mechanisms, and delivery efficiency to the brain are highlighted. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.

Occurrence of Morpholine in Central Nervous System Drug Discovery

Developing drugs for the central nervous system (CNS) requires fine chemical modifications, as a strict balance between size and lipophilicity is necessary to improve the permeability through the blood-brain barrier (BBB). In this context, morpholine and its analogues represent valuable heterocycles, due to their conformational and physicochemical properties. In fact, the presence of a weak basic nitrogen atom and of an oxygen atom at the opposite position provides a peculiar pKa value and a flexible conformation to the ring, thus allowing it to take part in several lipophilic–hydrophilic interactions, and to improve blood solubility and brain permeability of the overall structure. In CNS-active compounds, morpholines are used (1) to enhance the potency through molecular interactions, (2) to act as a scaffold directing the appendages in the correct position, and (3) to modulate pharmacokinetic/pharmacodynamic (PK/PD) properties. In this perspective, selected morpholine-containing CNS drug candidates are discussed to reveal the active pharmacophores accountable for the (1) modulation of receptors involved in mood disorders and pain, (2) bioactivity toward enzymes and receptors responsible for neurodegenerative diseases, and (3) inhibition of enzymes involved in the pathology of CNS tumors. The medicinal chemistry/pharmacological activity of morpholine derivatives is discussed, in the effort to highlight the importance of morpholine ring interactions in the active site of different targets, particularly reporting binding features retrieved from PDB data, when available.