Central Nervous System Drug Development 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.

Identification of (R)-[18F]YH134 for Monoacylglycerol Lipase Neuroimaging and Exploration of Its Use for Central Nervous System and Peripheral Drug Development.

This study aimed to evaluate (R)-[18F]YH134 as a novel PET tracer for imaging monoacylglycerol lipase (MAGL). Considering the ubiquitous expression of MAGL throughout the whole body, the impact of various MAGL inhibitors on (R)-[18F]YH134 brain uptake and its application in brain-periphery crosstalk were explored. Methods: MAGL knockout and wild-type mice were used to evaluate (R)-[18F]YH134 in invitro autoradiography and PET experiments. To explore the impact of peripheral MAGL occupancy on (R)-[18F]YH134 brain uptake, PET kinetics with an arterial input function were studied in male Wistar rats under baseline and blocking conditions. Results: In invitro autoradiography, (R)-[18F]YH134 revealed a heterogeneous distribution pattern with high binding to MAGL-rich brain regions in wild-type mouse brain slices, whereas the radioactive signal was negligible in MAGL knockout mouse brain slices. The invivo brain PET images of (R)-[18F]YH134 in wild-type and MAGL knockout mice demonstrated its high specificity and selectivity in mouse brain. A Logan plot with plasma input function was applied to estimate the distribution volume (V T) of (R)-[18F]YH134. V T was significantly reduced by a brain-penetrant MAGL inhibitor but was unchanged by a peripherally restricted MAGL inhibitor. The MAGL target occupancy in the periphery was estimated using (R)-[18F]YH134 PET imaging data from the brain. Conclusion: (R)-[18F]YH134 is a highly specific and selective PET tracer with favorable kinetic properties for imaging MAGL in rodent brain. Our results showed that blocking of the peripheral target influences brain uptake but not the V T of (R)-[18F]YH134. (R)-[18F]YH134 can be used for estimating the dose of MAGL inhibitor at half-maximal peripheral target occupancy.

Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds

It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.

Application of In vitro transcytosis models to brain targeted biologics.

The blood brain barrier (BBB) efficiently limits the penetration of biologics drugs from blood to brain. Establishment of an in vitro BBB model can facilitate screening of central nervous system (CNS) drug candidates and accelerate CNS drug development. Despite many established in vitro models, their application to biologics drug selection has been limited. Here, we report the evaluation of in vitro transcytosis of anti-human transferrin receptor (TfR) antibodies across human, cynomolgus and mouse species. We first evaluated human models including human cerebral microvascular endothelial cell line hCMEC/D3 and human colon epithelial cell line Caco-2 models. hCMEC/D3 model displayed low trans-epithelial electrical resistance (TEER), strong paracellular transport, and similar transcytosis of anti-TfR and control antibodies. In contrast, the Caco-2 model displayed high TEER value and low paracellular transport. Anti-hTfR antibodies demonstrated up to 70-fold better transcytosis compared to control IgG. Transcytosis of anti-hTfR.B1 antibody in Caco-2 model was dose-dependent and saturated at 3 μg/mL. Enhanced transcytosis of anti-hTfR.B1 was also observed in a monkey brain endothelial cell based (MBT) model. Importantly, anti-hTfR.B1 showed relatively high brain radioactivity concentration in a non-human primate positron emission tomography study indicating that the in vitro transcytosis from both Caco-2 and MBT models aligns with in vivo brain exposure. Typically, brain exposure of CNS targeted biologics is evaluated in mice. However, antibodies, such as the anti-human TfR antibodies, do not cross-react with the mouse target. Therefore, validation of a mouse in vitro transcytosis model is needed to better understand the in vitro in vivo correlation. Here, we performed transcytosis of anti-mouse TfR antibodies in mouse brain endothelial cell-based models, bEnd3 and the murine intestinal epithelial cell line mIEC. There is a good correlation between in vitro transcytosis of anti-mTfR antibodies and bispecifics in mIEC model and their mouse brain uptake. These data strengthen our confidence in the predictive power of the in vitro transcytosis models. Both mouse and human in vitro models will serve as important screening assays for brain targeted biologics selection in CNS drug development.

Metabotropic Glutamate Receptor Subtype 5 Positron-Emission-Tomography Radioligands as a Tool for Central Nervous System Drug Development: Between Progress and Setbacks.

The metabotropic glutamate receptor subtype 5 (mGluR5) is a class C G-protein-coupled receptor (GPCR) that has been implicated in various neuronal processes and, consequently, in several neuropsychiatric or neurodevelopmental disorders. Over the past few decades, mGluR5 has become a major focus for pharmaceutical companies, as an attractive target for drug development, particularly through the therapeutic potential of its modulators. In particular, allosteric binding sites have been targeted for better specificity and efficacy. In this context, Positron Emission Tomography (PET) appears as a useful tool for making decisions along a drug candidate's development process, saving time and money. Thus, PET provides quantitative information about a potential drug candidate and its target at the molecular level. However, in this area, particular attention has to be given to the interpretation of the PET signal and its conclusions. Indeed, the complex pharmacology of both mGluR5 and radioligands, allosterism, the influence of endogenous glutamate and the choice of pharmacokinetic model are all factors that may influence the PET signal. This review focuses on mGluR5 PET radioligands used at several stages of central nervous system drug development, highlighting advances and setbacks related to the complex pharmacology of these radiotracers.

Overcoming the blood-brain barrier? - prediction of blood-brain permeability of hydrophobically modified polyethylenimine polyplexes for siRNA delivery into the brain with in vitro and in vivo models

The blood-brain barrier (BBB) is a highly selective biological barrier that represents a major bottleneck in the treatment of all types of central nervous system (CNS) disorders. Small interfering RNA (siRNA) offers in principle a promising therapeutic approach, e.g., for brain tumors, by downregulating brain tumor-related genes and inhibiting tumor growth via RNA interference. In an effort to develop efficient siRNA nanocarriers for crossing the BBB, we utilized polyethyleneimine (PEI) polymers hydrophobically modified with either stearic-acid (SA) or dodecylacrylamide (DAA) subunits and evaluated their suitability for delivering siRNA across the BBB in in vitro and in vivo BBB models depending on their structure. Physicochemical characteristics of siRNA-polymer complexes (polyplexes (PXs)), e.g., particle size and surface charge, were measured by dynamic light scattering and laser Doppler anemometry, whereas siRNA condensation ability of polymers and polyplex stability was evaluated by spectrophotometric methods. The composition of the biomolecule corona that absorbs on polyplexes upon encountering physiological fluids was investigated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and by a liquid chromatography-tandem mass spectrometry (LC-MS-MS) method. Cellular internalization abilities of PXs into brain endothelial cells (hCMEC/D3) was confirmed, and a BBB permeation assay using a human induced pluripotent stem cell (hiPSC)–derived BBB model revealed similar abilities to cross the BBB for all formulations under physiological conditions. However, biodistribution studies of radiolabeled PXs in mice were inconsistent with in vitro results as the detected amount of radiolabeled siRNA in the brain delivered with PEI PXs was higher compared to PEI-SA PXs. Taken together, PEI PXs were shown to be a suitable nanocarrier to deliver small amounts of siRNA across the BBB into the brain but more sophisticated human BBB models that better represent physiological conditions and biodistribution are required to provide highly predictive in vitro data for human CNS drug development in the future.

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.

Intranasal Peptide Therapeutics: A Promising Avenue for Overcoming the Challenges of Traditional CNS Drug Development.

The central nervous system (CNS) has, among all organ systems in the human body, the highest failure rate of traditional small-molecule drug development, ranging from 80-100% depending on the area of disease research. This has led to widespread abandonment by the pharmaceutical industry of research and development for CNS disorders, despite increased diagnoses of neurodegenerative disorders and the continued lack of adequate treatment options for brain injuries, stroke, neurodevelopmental disorders, and neuropsychiatric illness. However, new approaches, concurrent with the development of sophisticated bioinformatic and genomic tools, are being used to explore peptide-based therapeutics to manipulate endogenous pathways and targets, including "undruggable" intracellular protein-protein interactions (PPIs). The development of peptide-based therapeutics was previously rejected due to systemic off-target effects and poor bioavailability arising from traditional oral and systemic delivery methods. However, targeted nose-to-brain, or intranasal (IN), approaches have begun to emerge that allow CNS-specific delivery of therapeutics via the trigeminal and olfactory nerve pathways, laying the foundation for improved alternatives to systemic drug delivery. Here we review a dozen promising IN peptide therapeutics in preclinical and clinical development for neurodegenerative (Alzheimer's, Parkinson's), neuropsychiatric (depression, PTSD, schizophrenia), and neurodevelopmental disorders (autism), with insulin, NAP (davunetide), IGF-1, PACAP, NPY, oxytocin, and GLP-1 agonists prominent among them.

A merged molecular representation deep learning method for blood–brain barrier permeability prediction

The ability of a compound to permeate across the blood-brain barrier (BBB) is a significant factor for central nervous system drug development. Thus, for speeding up the drug discovery process, it is crucial to perform high-throughput screenings to predict the BBB permeability of the candidate compounds. Although experimental methods are capable of determining BBB permeability, they are still cost-ineffective and time-consuming. To complement the shortcomings of existing methods, we present a deep learning-based multi-model framework model, called Deep-B3, to predict the BBB permeability of candidate compounds. In Deep-B3, the samples are encoded in three kinds of features, namely molecular descriptors and fingerprints, molecular graph and simplified molecular input line entry system (SMILES) text notation. The pre-trained models were built to extract latent features from the molecular graph and SMILES. These features depicted the compounds in terms of tabular data, image and text, respectively. The validation results yielded from the independent dataset demonstrated that the performance of Deep-B3 is superior to that of the state-of-the-art models. Hence, Deep-B3 holds the potential to become a useful tool for drug development. A freely available online web-server for Deep-B3 was established at http://cbcb.cdutcm.edu.cn/deepb3/, and the source code and dataset of Deep-B3 are available at https://github.com/GreatChenLab/Deep-B3.

Blood–brain barrier penetration prediction enhanced by uncertainty estimation

Blood–brain barrier is a pivotal factor to be considered in the process of central nervous system (CNS) drug development, and it is of great significance to rapidly explore the blood–brain barrier permeability (BBBp) of compounds in silico in early drug discovery process. Here, we focus on whether and how uncertainty estimation methods improve in silico BBBp models. We briefly surveyed the current state of in silico BBBp prediction and uncertainty estimation methods of deep learning models, and curated an independent dataset to determine the reliability of the state-of-the-art algorithms. The results exhibit that, despite the comparable performance on BBBp prediction between graph neural networks-based deep learning models and conventional physicochemical-based machine learning models, the GROVER-BBBp model shows greatly improvement when using uncertainty estimations. In particular, the strategy combined Entropy and MC-dropout can increase the accuracy of distinguishing BBB + from BBB − to above 99% by extracting predictions with high confidence level (uncertainty score < 0.1). Case studies on preclinical/clinical drugs for Alzheimer’ s disease and marketed antitumor drugs that verified by literature proved the application value of uncertainty estimation enhanced BBBp prediction model, that may facilitate the drug discovery in the field of CNS diseases and metastatic brain tumors.

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.

Targeted Drug Delivery to the Central Nervous System Using Extracellular Vesicles.

The blood brain barrier (BBB) maintains the homeostasis of the central nervous system (CNS) and protects the brain from toxic substances present in the circulating blood. However, the impermeability of the BBB to drugs is a hurdle for CNS drug development, which hinders the distribution of the most therapeutic molecules into the brain. Therefore, scientists have been striving to develop safe and effective technologies to advance drug penetration into the CNS with higher targeting properties and lower off-targeting side effects. This review will discuss the limitation of artificial nanomedicine in CNS drug delivery and the use of natural extracellular vesicles (EVs), as therapeutic vehicles to achieve targeted delivery to the CNS. Information on clinical trials regarding CNS targeted drug delivery using EVs is very limited. Thus, this review will also briefly highlight the recent clinical studies on targeted drug delivery in the peripheral nervous system to shed light on potential strategies for CNS drug delivery. Different technologies engaged in pre- and post-isolation have been implemented to further utilize and optimize the natural property of EVs. EVs from various sources have also been applied in the engineering of EVs for CNS targeted drug delivery in vitro and in vivo. Here, the future feasibility of those studies in clinic will be discussed.

Challenges in central nervous system drug development：What pharmaceutical corporations are seeking

The science and technology infrastructure in drug discovery for central nervous system (CNS) diseases and disorders has been greatly developed over the past decade. Various hypotheses have been proposed about the pathogenesis and progression of CNS diseases and disorders, and efforts to develop new drugs for rare diseases are also becoming active. The drug modalities have expanded from small molecules to medium molecules, antibodies, proteins, nucleic acids, cells, and even digital, and the drug discovery process is changing dramatically. Drug discovery researchers require a wide variety of specialized knowledge and techniques. As research tools such as human induced pluripotent stem cells and genetically modified animals become increasingly complex, it is often difficult to prepare and procure them within a pharmaceutical corporation. Furthermore, patient stratification in clinical studies and translational research to predict the efficacy of drug candidates have become important pillars in drug discovery research. Under such circumstances, it is becoming difficult for pharmaceutical corporations to complete all elements necessary for drug discovery research on their own, and collaboration with partners with expertise is becoming essential to improve the quality and speed of research. In this presentation, I would like to introduce what corporations are seeking, hoping that this symposium will generate some ideas for new approaches and collaborations.

Drug delivery to the central nervous system.

Despite the rising global incidence of central nervous system (CNS) disorders, CNS drug development remains challenging, with high costs, long pathways to clinical use and high failure rates. The CNS is highly protected by physiological barriers, in particular, the blood-brain barrier and the blood-cerebrospinal fluid barrier, which limit access of most drugs. Biomaterials can be designed to bypass or traverse these barriers, enabling the controlled delivery of drugs into the CNS. In this Review, we first examine the effects of normal and diseased CNS physiology on drug delivery to the brain and spinal cord. We then discuss CNS drug delivery designs and materials that are administered systemically, directly to the CNS, intranasally or peripherally through intramuscular injections. Finally, we highlight important challenges and opportunities for materials design for drug delivery to the CNS and the anticipated clinical impact of CNS drug delivery.

Scorpion Venom Heat-Resistant Peptide Attenuates Microglia Activation and Neuroinflammation.

Background: Intervention of neuroinflammation in central nervous system (CNS) represents a potential therapeutic strategy for a host of brain disorders. The scorpion Buthus martensii Karsch (BmK) and its venom have long been used in the Orient to treat inflammation-related diseases such as rhumatoid arthritis and chronic pain. Scorpion venom heat-resistant peptide (SVHRP), a component from BmK venom, has been shown to reduce seizure susceptibility in a rat epileptic model and protect against cerebral ischemia-reperfusion injury. As neuroinflammation has been implicated in chronic neuronal hyperexcitability, epileptogenesis and cerebral ischemia-reperfusion injury, the present study aimed to investigate whether SVHRP has anti-inflammatory property in brain. Methods: An animal model of neuroinflammation induced by lipopolysacchride (LPS) injection was employed to investigate the effect of SVHRP (125 µg/kg, intraperitoneal injection) on inflammagen-induced expression of pro-inflammatory factors and microglia activation. The effect of SVHRP (2–20 μg/ml) on neuroinflammation was further investigated in primary brain cell cultures containing microglia as well as the immortalized BV2 microglia culture stimulated with LPS. Real-time quantitative PCR were used to measure mRNA levels of inducible nitric oxide synthase (iNOS), tumor necrosis factor-α (TNF-α), interleukin (IL)-1β and IL-6 in hippocampus of animals. Protein levels of TNF-α, iNOS, P65 subunit of nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs) were examined by ELISA or western blot. Microglia morphology in animal hippocampus or cell cultures and cellular distribution of p65 were shown by immunostaining. Results: Morphological study demonstrated that activation of microglia, the main component that mediates the neuroinflammatory process, was inhibited by SVHRP in both LPS mouse and cellular model. Our results also showed dramatic increases in the expression of iNOS and TNF-α in hippocampus of LPS-injected mice, which was significantly attenuated by SVHRP treatment. In vitro results showed that SVHRP attenuated LPS-elicited expression of iNOS and TNF-α in different cultures without cell toxicity, which might be attributed to suppression of NF-κB and MAPK pathways by SVHRP. Conclusion: Our study demonstrates that SVHRP is able to inhibit neuroinflammation and microglia activation, which may underlie the therapeutic effects of BmK-derived materials, suggesting that BmK venom could be a potential source for CNS drug development.

Drug Penetration into the Central Nervous System: Pharmacokinetic Concepts and In Vitro Model Systems.

Delivery of most drugs into the central nervous system (CNS) is restricted by the blood–brain barrier (BBB), which remains a significant bottleneck for development of novel CNS-targeted therapeutics or molecular tracers for neuroimaging. Consistent failure to reliably predict drug efficiency based on single measures for the rate or extent of brain penetration has led to the emergence of a more holistic framework that integrates data from various in vivo, in situ and in vitro assays to obtain a comprehensive description of drug delivery to and distribution within the brain. Coupled with ongoing development of suitable in vitro BBB models, this integrated approach promises to reduce the incidence of costly late-stage failures in CNS drug development, and could help to overcome some of the technical, economic and ethical issues associated with in vivo studies in animal models. Here, we provide an overview of BBB structure and function in vivo, and a summary of the pharmacokinetic parameters that can be used to determine and predict the rate and extent of drug penetration into the brain. We also review different in vitro models with regard to their inherent shortcomings and potential usefulness for development of fast-acting drugs or neurotracers labeled with short-lived radionuclides. In this regard, a special focus has been set on those systems that are sufficiently well established to be used in laboratories without significant bioengineering expertise.

Strategies for Structural Modification of Small Molecules to Improve Blood–Brain Barrier Penetration: A Recent Perspective

In the development of central nervous system (CNS) drugs, the blood-brain barrier (BBB) restricts many drugs from entering the brain to exert therapeutic effects. Although many novel delivery methods of large molecule drugs have been designed to assist transport, small molecule drugs account for the vast majority of the CNS drugs used clinically. From this perspective, we review studies from the past five years that have sought to modify small molecules to increase brain exposure. Medicinal chemists make it easier for small molecules to cross the BBB by improving diffusion, reducing efflux, and activating carrier transporters. On the basis of their excellent work, we summarize strategies for structural modification of small molecules to improve BBB penetration. These strategies are expected to provide a reference for the future development of small molecule CNS drugs.

Prodrug Strategies in the CNS Drugs: Small Modification Makes Big Improvements.

Prodrug design is an effective method proven to improve the drug-like properties of a molecule, and it has been widely used in the drug development of various diseases. Due to the complexity of the central nervous system (CNS), the development of CNS drugs has high requirements related to the pharmaceutical, pharmacokinetic, and pharmacodynamic properties of the molecules. Prodrug design has now been widely and successfully applied to improve these properties. We conducted a mini-review to promote the use of the prodrug strategies in CNS drug development. To facilitate the description, we chose drug indications as a clue, then presented and discussed some representative CNS prodrugs. Finally, a brief summary and outlook about this area were presented.

UPLC-MS/MS-based profiling of 31 neurochemicals in the mouse brain after treatment with the antidepressant nefazodone

Depression, typically understood as a mental disorder, is characterized by monoamine neurotransmitter deficiencies in the central nervous system (CNS). A majority of antidepressants have been developed to increase endogenous levels of monoamine neurotransmitters, such as dopamine (DA), norepinephrine (NE), and serotonin (5-HT) in brain tissue. Increasingly, the critical effects of a variety of neurotransmitters, including those of the dopaminergic, serotonergic, kynurenergic, cholinergic, histaminergic, and GABAergic systems, as well as several amino acids, have been spotlighted as neuromodulators over the years in various behavioral and emotional responses. In the present study, we developed a simple method for simultaneous analysis of 31 neurochemicals in mouse whole brain tissue using liquid chromatography tandem mass spectrometry (LC-MS/MS). Our neurochemical profiles were classified into seven categories according to metabolic pathways composed of neurotransmitters and their precursors or metabolites. The analytical method was validated by intra- and inter-day precision, accuracy, sensitivity, and selectivity, and finally applied for neurochemical profiling in the mouse brain following nefazodone treatment. In this study, the modulation of 5-HT and DA by nefazodone was confirmed in the mouse brain, as previously reported. Interestingly, nefazodone-induced broad-spectrum neurochemical changes could suggest that nefazodone has novel anti-depressive and anti-cholinesterase potency via increasing endogenous levels of betaine and acetylcholine in the mouse brain. This neurochemical profiling approach may represent a meaningful tool for better understanding brain neurochemistry and neurotoxicity in discovery and development of CNS drugs.

Lumbar cerebrospinal fluid-to-brain extracellular fluid surrogacy is context-specific: insights from LeiCNS-PK3.0 simulations

Predicting brain pharmacokinetics is critical for central nervous system (CNS) drug development yet difficult due to ethical restrictions of human brain sampling. CNS pharmacokinetic (PK) profiles are often altered in CNS diseases due to disease-specific pathophysiology. We previously published a comprehensive CNS physiologically-based PK (PBPK) model that predicted the PK profiles of small drugs at brain and cerebrospinal fluid compartments. Here, we improved this model with brain non-specific binding and pH effect on drug ionization and passive transport. We refer to this improved model as Leiden CNS PBPK predictor V3.0 (LeiCNS-PK3.0). LeiCNS-PK3.0 predicted the unbound drug concentrations of brain ECF and CSF compartments in rats and humans with less than two-fold error. We then applied LeiCNS-PK3.0 to study the effect of altered cerebrospinal fluid (CSF) dynamics, CSF volume and flow, on brain extracellular fluid (ECF) pharmacokinetics. The effect of altered CSF dynamics was simulated using LeiCNS-PK3.0 for six drugs and the resulting drug exposure at brain ECF and lumbar CSF were compared. Simulation results showed that altered CSF dynamics changed the CSF PK profiles, but not the brain ECF profiles, irrespective of the drug’s physicochemical properties. Our analysis supports the notion that lumbar CSF drug concentration is not an accurate surrogate of brain ECF, particularly in CNS diseases. Systems approaches account for multiple levels of CNS complexity and are better suited to predict brain PK.