Structure-brain exposure relationships in rat and human using a novel data set of unbound drug concentrations in brain interstitial and cerebrospinal fluids

Potentially Life-Threatening Interaction between Opioids and Intrathecal Baclofen in Individuals with a Childhood-Onset Neurological Disorder: A Case Series and Review of the Literature

BACKGROUND

Spasticity and dystonia are movement impairments that can occur in childhood-onset neurological disorders. Severely affected individuals can be treated with intrathecal baclofen (ITB). Concomitant use of ITB and opioids has been associated with central nervous system (CNS) depression. This study aims to describe the clinical management of this interaction, based on a case series and review of literature.

METHODS

Four individuals with childhood-onset CNS disorders (age 8-24) and CNS-depressant overdose symptoms after the concomitant use of ITB and opioids are described. The Drug Interaction Probability Scale (DIPS) was calculated to assess the cause-relationship (doubtful <2, possible 2-4, probable 5-8, and highly probable >8) of the potential drug-drug interaction. A literature review of similar previously reported cases and the possible pharmacological mechanisms of opioid-baclofen interaction is provided.

RESULTS

After ITB and opioid co-administration, three out of four patients had decreased consciousness, and three developed respiratory depression. DIPS scores indicated a possible cause-relationship in one patient (DIPS: 4) and a probable cause-relationship in the others (DIPS: 6, 6, and 8). Discontinuation or adjusting ITB or opioid dosages resulted in clinical recovery. All patients recovered completely. In the literature, two articles describing nine unique cases were found.

CONCLUSION

Although the opioid-ITB interaction is incompletely understood, concomitant use may enhance the risk of symptoms of CNS-depressant overdose, which are potentially life-threatening. If concomitant use is desirable, we strongly recommend to closely monitor these patients to detect interaction symptoms early. Awareness and monitoring of the potential opioid-ITB interaction is essential to reduce the risk of severe complications.

Optimization of Potent, Efficacious, Selective and Blood-Brain Barrier Penetrating Inhibitors Targeting EGFR Exon20 Insertion Mutations

Herein, we report the optimization of a series of epidermal growth factor receptor (EGFR) Exon20 insertion (Ex20Ins) inhibitors using structure-based drug design (SBDD), leading to the discovery of compound 28, a potent and wild type selective molecule, which demonstrates efficacy in multiple EGFR Ex20Ins xenograft models and blood-brain barrier penetration in preclinical species. Building on our earlier discovery of an in vivo probe, SBDD was used to design a novel bicyclic core with a lower molecular weight to facilitate blood-brain barrier penetration. Further optimization including strategic linker replacement and diversification of the ring system interacting with the c-helix enabled photolytic and metabolic stability improvements. Together with refinement of molecular properties important for achieving high brain exposure, including molecular weight, H-bonding, and polarity, 28 was identified.

Preclinical evaluation of targeted therapies for central nervous system metastases

The central nervous system (CNS) represents a site of sanctuary for many metastatic tumors when systemic therapies that control the primary tumor cannot effectively penetrate intracranial lesions. Non-small cell lung cancers (NSCLCs) are the most likely of all neoplasms to metastasize to the brain, with up to 60% of patients developing CNS metastases during the disease process. Targeted therapies such as tyrosine kinase inhibitors (TKIs) have helped reduce lung cancer mortality but vary considerably in their capacity to control CNS metastases. The ability of these therapies to effectively target lesions in the CNS depends on several of their pharmacokinetic properties, including blood-brain barrier permeability, affinity for efflux transporters, and binding affinity for both plasma and brain tissue. Despite the existence of numerous preclinical models with which to characterize these properties, many targeted therapies have not been rigorously tested for CNS penetration during the discovery process, whereas some made it through preclinical testing despite poor brain penetration kinetics. Several TKIs have now been engineered with the characteristics of CNS-penetrant drugs, with clinical trials proving these efforts fruitful. This Review outlines the extent and variability of preclinical evidence for the efficacy of NSCLC-targeted therapies, which have been approved by the US Food and Drug Administration (FDA) or are in development, for treating CNS metastases, and how these data correlate with clinical outcomes.

Synergistic induction of blood-brain barrier properties

Blood-brain barrier (BBB) models derived from human stem cells are powerful tools to improve our understanding of cerebrovascular diseases and to facilitate drug development for the human brain. Yet providing stem cell-derived endothelial cells with the right signaling cues to acquire BBB characteristics while also retaining their vascular identity remains challenging. Here, we show that the simultaneous activation of cyclic AMP and Wnt/β-catenin signaling and inhibition of the TGF-β pathway in endothelial cells robustly induce BBB properties in vitro. To target this interaction, we present a small-molecule cocktail named cARLA, which synergistically enhances barrier tightness in a range of BBB models across species. Mechanistically, we reveal that the three pathways converge on Wnt/β-catenin signaling to mediate the effect of cARLA via the tight junction protein claudin-5. We demonstrate that cARLA shifts the gene expressional profile of human stem cell-derived endothelial cells toward the in vivo brain endothelial signature, with a higher glycocalyx density and efflux pump activity, lower rates of endocytosis, and a characteristic endothelial response to proinflammatory cytokines. Finally, we illustrate how cARLA can improve the predictive value of human BBB models regarding the brain penetration of drugs and targeted nanoparticles. Due to its synergistic effect, high reproducibility, and ease of use, cARLA has the potential to advance drug development for the human brain by improving BBB models across laboratories.

Designing drugs optimized for both blood-brain barrier permeation and intra-cerebral partition

INTRODUCTION

With the increasing incidence and prevalence of neurological disorders globally, there is a paramount need for new pharmacotherapies. BBB effectively protects the brain but raises a profound challenge to drug permeation, with less than 2% of most drugs reaching the CNS.

AREAS COVERED

This article reviews aspects of the most recent design strategies, providing insights into ideas and concepts in CNS drug discovery. An overview of the products available on the market is given and why clinical trials are continuously failing is discussed.

EXPERT OPINION

Among the available CNS drugs, small molecules account for most successful CNS therapeutics due to their ability to penetrate the BBB through passive or carrier-mediated mechanisms. The development of new CNS drugs is very difficult. To date, there is a lack of effective drugs for alleviating or even reversing the progression of brain diseases. Particularly, the use of artificial intelligence strategies, together with more appropriate animal models, may enable the design of molecules with appropriate permeation, to elicit a biological response from the neurotherapeutic target.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

Accurate prediction of Kp,uu,brain based on experimental measurement of Kp,brain and computed physicochemical properties of candidate compounds in CNS drug discovery

A mathematical equation model was developed by building the relationship between the fu,b/fu,p ratio and the computed physicochemical properties of candidate compounds, thereby predicting Kp,uu,brain based on a single experimentally measured Kp,brain value. A total of 256 compounds and 36 marketed published drugs including acidic, basic, neutral, zwitterionic, CNS-penetrant, and non-CNS penetrant compounds with diverse structures and physicochemical properties were involved in this study. A strong correlation was demonstrated between the fu,b/fu,p ratio and physicochemical parameters (CLogP and ionized fraction). The model showed good performance in both internal and external validations. The percentages of compounds with Kp,uu,brain predictions within 2-fold variability were 80.0 %-83.3 %, and more than 90 % were within a 3-fold variability. Meanwhile, "black box" QSAR models constructed by machine learning approaches for predicting fu,b/fu,p ratio based on the chemical descriptors are also presented, and the ANN model displayed the highest accuracy with an RMSE value of 0.27 and 86.7 % of the test set drugs fell within a 2-fold window of linear regression. These models demonstrated strong predictive power and could be helpful tools for evaluating the Kp,uu,brain by a single measurement parameter of Kp,brain during lead optimization for CNS penetration evaluation and ranking CNS drug candidate molecules in the early stages of CNS drug discovery.

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

Assessing extent of brain penetration in vivo (Kp,uu,brain) in Göttingen minipig using a diverse set of reference drugs

The application of Göttingen minipigs for non-rodent pharmacokinetics (PK) and drug safety testing has seen a dramatic increase in recent years. The aim of this study was to determine the total and unbound brain-to-plasma ratios (Kp,brain and Kp,uu,brain) for a diverse set of reference compounds in female Göttingen minipigs and compare these with Kp,uu,brain values from other species to assess the suitability of Göttingen minipigs as a model for CNS drug safety testing and brain PK in clinical translation. The reference set consisted of 17 compounds with varying physico-chemical properties and included known human P-glycoprotein (P-gp) substrates. The results of the study showed, that minipig Kp,brain and Kp,uu,brain values for the tested compounds were in the range 0.03-86 and 0.02-2.4 (n = 3-4) respectively. The Kp,uu,brain values were comparable between minipig and rat for a large proportion of the compounds (71% within 2-fold, n = 17). Comparisons of brain penetration across several species for a subset of reference compounds revealed that minipig values were quite similar to those of rat, dog, monkey and human. The study highlighted that the largest Kp,uu,brain species differences were observed for compounds classified as transporter substrates (e.g. cimetidine, risperidone, Way-100635 and altanserin). In conclusion these brain penetration data add substantially to the available literature on PK and drug disposition for minipigs and support use of Göttingen minipig as a non-rodent drug safety model for CNS drug candidates and as a brain PK model for clinical translation.

Uptake Transporters at the Blood-Brain Barrier and Their Role in Brain Drug Disposition

Uptake drug transporters play a significant role in the pharmacokinetic of drugs within the brain, facilitating their entry into the central nervous system (CNS). Understanding brain drug disposition is always challenging, especially with respect to preclinical to clinical translation. These transporters are members of the solute carrier (SLC) superfamily, which includes organic anion transporter polypeptides (OATPs), organic anion transporters (OATs), organic cation transporters (OCTs), and amino acid transporters. In this systematic review, we provide an overview of the current knowledge of uptake drug transporters in the brain and their contribution to drug disposition. Here, we also assemble currently available proteomics-based expression levels of uptake transporters in the human brain and their application in translational drug development. Proteomics data suggest that in association with efflux transporters, uptake drug transporters present at the BBB play a significant role in brain drug disposition. It is noteworthy that a significant level of species differences in uptake drug transporters activity exists, and this may contribute toward a disconnect in inter-species scaling. Taken together, uptake drug transporters at the BBB could play a significant role in pharmacokinetics (PK) and pharmacodynamics (PD). Continuous research is crucial for advancing our understanding of active uptake across the BBB.

Optimizing transcardial perfusion of small molecules and biologics for brain penetration and biodistribution studies in rodents

Efficiently removing blood from the brain vasculature is critical to evaluate accurately the brain penetration and biodistribution of drug candidates, especially for biologics as their blood concentrations are substantially higher than the brain concentrations. Transcardial perfusion has been used widely to remove residual blood in the brain; however, the perfusion conditions (such as the perfusion rate and time) reported in the literature are quite varied, and the performance of these methods on blood removal has not been investigated thoroughly. In this study, the effectiveness of the perfusion conditions was assessed by measuring brain hemoglobin levels. Sodium nitrite (NaNO₂ ) as an additive in the perfusate was evaluated at different concentrations. Blood removal was significantly improved with 2% NaNO₂ over a 20 min perfusion in mouse without disrupting the integrity of the blood-brain barrier (BBB). In mice, the optimized perfusion method significantly lowered the measured brain-to-plasma ratio (K_p,brain ) for monoclonal antibodies due to the removal of blood contamination and small molecules with a moderate-to-high BBB permeability and with a high brain-unbound-fraction (f_u,brain ) presumably due to flux out of the brain during perfusion. Perfusion with or without NaNO₂ clearly removed the residual blood in rat brain but with no difference observed in K_p,brain between the perfusion groups with or without 2% NaNO₂ . In conclusion, a perfusion method was successfully developed to evaluate the brain penetration of small molecules and biologics in rodents for the first time. The transcardial perfusion with 2% NaNO₂ effectively removed the residual blood in the brain and significantly improved the assessment of brain penetration of biologics. For small molecules, however, transcardial perfusion may not be performed, as small molecule compounds could be washed away from the brain by the perfusion procedure.

Developmental changes in the extent of drug binding to rat plasma proteins

Binding of therapeutics to proteins in blood plasma is important in influencing their distribution as it is their free (unbound) form that is able to cross cellular membranes to enter tissues and exert their actions. The concentration and composition of plasma proteins vary during pregnancy and development, resulting in potential changes to drug protein binding. Here, we describe an ultrafiltration method to investigate the extent of protein binding of six drugs (digoxin, paracetamol, olanzapine, ivacaftor, valproate and lamotrigine) and two water soluble inert markers (sucrose and glycerol) to plasma proteins from pregnant and developing rats. Results showed that the free fraction of most drugs was lower in the non-pregnant adult plasma where protein concentration is the highest. However, plasma of equivalent protein concentration to younger pups obtained by diluting adult plasma did not always exhibit the same extent of drug binding, reinforcing the likelihood that both concentration and composition of proteins in plasma influence drug binding. Comparison between protein binding and brain drug accumulation in vivo revealed a correlation for some drugs, but not others. Results suggests that plasma protein concentration should be considered when using medications in pregnant and paediatric patients to minimise potential for fetal and neonatal drug exposure.

Structural Alterations of the "Address" Moiety of NAN Leading to the Discovery of a Novel Opioid Receptor Modulator with Reduced hERG Toxicity

The search for selective opioid ligands with desired pharmacological potency and improved safety profile has always been an area of interest. Our previous effort yielded a potent opioid modulator, NAN, a 6α-N-7'-indolyl-substituted naltrexamine derivative, which exhibited promising pharmacological activities both in vitro and in vivo. However, significant human ether-a-go-go-related gene (hERG) liability limited its further development. Therefore, a systematic structural modification on NAN was conducted in order to alleviate hERG toxicity while preserving pharmacological properties, which led to the discovery of 2'-methylindolyl derivative compound 21. Compared to NAN, compound 21 manifested overall improved pharmacological profiles. Follow-up hERG channel inhibition evaluation revealed a seven-fold decreased potency of compound 21 compared to NAN. Furthermore, several fundamental drug-like property evaluations suggested a reasonable ADME profile of 21. Collectively, compound 21 appeared to be a promising opioid modulator for further development as a novel therapeutic agent toward opioid use disorder treatments.

QSAR model to predict Kp,uu,brain with a small dataset, incorporating predicted values of related parameter

The unbound brain-to-plasma concentration ratio (K_p,uu,brain) is a parameter that indicates the extent of central nervous system penetration. Pharmaceutical companies build prediction models because many experiments are required to obtain K_p,uu,brain. However, the lack of data hinders the design of an accurate prediction model. To construct a quantitative structure-activity relationship (QSAR) model with a small dataset of K_p,uu,brain, we investigated whether the prediction accuracy could be improved by incorporating software-predicted brain penetration-related parameters (BPrPs) as explanatory variables for pharmacokinetic parameter prediction. We collected 88 compounds with experimental K_p,uu,brain from various official publications. Random forest was used as the machine learning model. First, we developed prediction models using only structural descriptors. Second, we verified the predictive accuracy of each model with the predicted values of BPrPs incorporated in various combinations. Third, the K_p,uu,brain of the in-house compounds was predicted and compared with the experimental values. The prediction accuracy was improved using five-fold cross-validation (RMSE = 0.455, r² = 0.726) by incorporating BPrPs. Additionally, this model was verified using an external in-house dataset. The result suggested that using BPrPs as explanatory variables improve the prediction accuracy of the K_p,uu,brain QSAR model when the available number of datasets is small.

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.

The development and function of the brain barriers - an overlooked consideration for chemical toxicity

It is well known that the adult brain is protected from some infections and toxic molecules by the blood-brain and the blood-cerebrospinal fluid barriers. Contrary to the immense data collected in other fields, it is deeply entrenched in environmental toxicology that xenobiotics easily permeate the developing brain because these barriers are either absent or non-functional in the fetus and newborn. Here we review the cellular and physiological makeup of the brain barrier systems in multiple species, and discuss decades of experiments that show they possess functionality during embryogenesis. We next present case studies of two chemical classes, perfluoroalkyl substances (PFAS) and bisphenols, and discuss their potential to bypass the brain barriers. While there is evidence to suggest these pollutants may enter the developing and/or adult brain parenchyma, many studies suffer from confounding technical variables which complicates data interpretation. In the future, a more formal consideration of brain barrier biology could not only improve understanding of chemical toxicokinetics but could assist in prioritizing environmental xenobiotics for their neurotoxicity risk.

Brain-penetrant calcium channel blockers are associated with a reduced incidence of neuropsychiatric disorders

Calcium channel blockers (CCBs) differ in their ability to penetrate into the brain. Pharmacoepidemiological studies suggest that CCBs as a class may have beneficial effects on the risks and outcomes of some psychiatric and neurological disorders. It is plausible but unknown whether this effect relates to their brain penetrance. To address this, we used the TriNetX electronic health records network to identify people prescribed a brain-penetrant CCB (BP-CCB), or those given amlodipine, a CCB with low brain penetrability. We created cohorts of patients who, prior to first CCB exposure, either had to have, or could not have had, a recorded ICD-10 diagnosis in any of the following categories: psychotic disorder; affective disorder (including bipolar disorder and major depressive disorder); anxiety disorder; substance use disorder; sleep disorder; delirium; dementia, or movement disorder. Cohort pairs were propensity score matched for age, sex, race, blood pressure, body mass index, and a range of other variables. The outcomes were the incidence of these disorders measured over a two-year exposure period. Matched cohort sizes ranged from 17,896 to 49,987. In people with no prior history of psychiatric or neurodegenerative disorder, there was a significantly lower incidence of most disorders with BP-CCBs compared to amlodipine, with risk ratios ranging from 0.64 to 0.88 and an overall risk ratio of 0.88, i.e. a risk reduction of 12%. In people who did have a prior psychiatric or neurodegenerative diagnosis, differences were much smaller, but again showed lower risks for several disorders with BP-CCBs compared to amlodipine. The differences were somewhat more marked in women and in people less than 60 years old. Results were similar when comparing BP-CCBs with verapamil and diltiazem. We also compared BP-CCBs with angiotensin receptor blockers, and found an overall risk ratio of 0.94 in favour of BP-CCBs, but with differential effects across disorders including a higher risk of psychotic disorder and dementia, but a lower risk for anxiety and sleep disorders. In some analyses, there was evidence of residual confounding even after the extensive matching, in that negative control outcomes showed a reduced incidence with BP-CCBs relative to the comparator cohort. In summary, CCBs that readily penetrate the brain are associated with a lower incidence of neuropsychiatric disorders, especially first diagnoses, compared to CCBs which do not. This may reflect their blockade of neuronal voltage-gated calcium channels. The findings encourage repurposing trials using existing BP-CCBs, and suggest that novel BP-CCBs with enhanced and more selective central actions might have greater therapeutic potential for psychiatric and neurodegenerative disorders.

Modelling the Human Blood-Brain Barrier in Huntington Disease

While blood–brain barrier (BBB) dysfunction has been described in neurological disorders, including Huntington’s disease (HD), it is not known if endothelial cells themselves are functionally compromised when promoting BBB dysfunction. Furthermore, the underlying mechanisms of BBB dysfunction remain elusive given the limitations with mouse models and post mortem tissue to identify primary deficits. We established models of BBB and undertook a transcriptome and functional analysis of human induced pluripotent stem cell (iPSC)-derived brain-like microvascular endothelial cells (iBMEC) from HD patients or unaffected controls. We demonstrated that HD-iBMECs have abnormalities in barrier properties, as well as in specific BBB functions such as receptor-mediated transcytosis.

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.

Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain-a Game Changing Parameter for CNS Drug Discovery and Development

PURPOSE

More than 15 years have passed since the first description of the unbound brain-to-plasma partition coefficient (Kp,uu,brain) by Prof. Margareta Hammarlund-Udenaes, which was enabled by advancements in experimental methodologies including cerebral microdialysis. Since then, growing knowledge and data continue to support the notion that the unbound (free) concentration of a drug at the site of action, such as the brain, is the driving force for pharmacological responses. Towards this end, Kp,uu,brain is the key parameter to obtain unbound brain concentrations from unbound plasma concentrations.

METHODS

To understand the importance and impact of the Kp,uu,brain concept in contemporary drug discovery and development, a survey has been conducted amongst major pharmaceutical companies based in Europe and the USA. Here, we present the results from this survey which consisted of 47 questions addressing: 1) Background information of the companies, 2) Implementation, 3) Application areas, 4) Methodology, 5) Impact and 6) Future perspectives.

RESULTS AND CONCLUSIONS

From the responses, it is clear that the majority of the companies (93%) has established a common understanding across disciplines of the concept and utility of Kp,uu,brain as compared to other parameters related to brain exposure. Adoption of the Kp,uu,brain concept has been mainly driven by individual scientists advocating its application in the various companies rather than by a top-down approach. Remarkably, 79% of all responders describe the portfolio impact of Kp,uu,brain implementation in their companies as 'game-changing'. Although most companies (74%) consider the current toolbox for Kp,uu,brain assessment and its validation satisfactory for drug discovery and early development, areas of improvement and future research to better understand human brain pharmacokinetics/pharmacodynamics translation have been identified.

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.

Prediction Models for Brain Distribution of Drugs Based on Biomimetic Chromatographic Data

The development of high-throughput approaches for the valid estimation of brain disposition is of great importance in the early drug screening of drug candidates. However, the complexity of brain tissue, which is protected by a unique vasculature formation called the blood−brain barrier (BBB), complicates the development of robust in silico models. In addition, most computational approaches focus only on brain permeability data without considering the crucial factors of plasma and tissue binding. In the present study, we combined experimental data obtained by HPLC using three biomimetic columns, i.e., immobilized artificial membranes, human serum albumin, and α1-acid glycoprotein, with molecular descriptors to model brain disposition of drugs. Kp,uu,brain, as the ratio between the unbound drug concentration in the brain interstitial fluid to the corresponding plasma concentration, brain permeability, the unbound fraction in the brain, and the brain unbound volume of distribution, was collected from literature. Given the complexity of the investigated biological processes, the extracted models displayed high statistical quality (R2 > 0.6), while in the case of the brain fraction unbound, the models showed excellent performance (R2 > 0.9). All models were thoroughly validated, and their applicability domain was estimated. Our approach highlighted the importance of phospholipid, as well as tissue and protein, binding in balance with BBB permeability in brain disposition and suggests biomimetic chromatography as a rapid and simple technique to construct models with experimental evidence for the early evaluation of CNS drug candidates.

Shedding Light on the Blood-Brain Barrier Transport with Two-Photon Microscopy In Vivo

Treatment of brain disorders relies on efficient delivery of therapeutics to the brain, which is hindered by the blood-brain barrier (BBB). The work of Prof. Margareta Hammarlund-Udenaes was instrumental in understanding the principles of drug delivery to the brain and developing new tools to study it. Here, we show how some of the concepts developed in her research can be translated to in vivo 2-photon microscopy (2PM) studies of the BBB. We primarily focus on the methods developed in our laboratory to characterize the paracellular diffusion, adsorptive-mediated transcytosis, and receptor-mediated transcytosis of drug nanocarriers at the microscale, illustrating how 2PM can deepen our understanding of the mechanisms of drug delivery to the brain.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

Regional Differences in the Absolute Abundance of Transporters, Receptors and Tight Junction Molecules at the Blood-Arachnoid Barrier and Blood-Spinal Cord Barrier among Cervical, Thoracic and Lumbar Spines in Dogs

PURPOSE

The purpose of the present study was to quantitatively determine the expression of transporters, receptors and tight junction molecules at the blood-arachnoid barrier (BAB) and blood-spinal cord barrier (BSCB) in cervical, thoracic and lumbar spines from dogs.

METHODS

The expression levels of 31 transporters, 3 receptors, 1 tight junction protein, and 3 marker proteins in leptomeninges and capillaries isolated from spines (3 male and 2 female dogs) were determined by quantitative Targeted Absolute Proteomics (qTAP). The units were converted from fmol/μg protein to pmol/cm (absolute abundance at the BAB and the BSCB in a 1 cm section of spine).

RESULTS

The expression of MDR1 and BCRP were greater at the BSCB compared to the BAB (especially in the cervical cord), and the expressions at the lumbar BSCB were lower than that for the cervical BSCB. Among the organic anionic and cationic drug transporters, OAT1, OAT3, MRP1, OCT2 and MATE1/2 were detected only in the BAB, and not at the BSCB). The expression of these transporters was higher in the order: lumbar > thoracic > cervical BAB. The expressions of GLUT1, 4F2hc, EAAT1, 2, PEPT2, CTL1, and MCT1 at the BSCB of the cervical cord were higher than the corresponding values for the cervical BAB, and these values decreased in going down the spinal cord.

CONCLUSION

These results provide a better understanding of the molecular mechanisms underlying the concentration gradients of drugs and endogenous substances in the cerebrospinal fluid and parenchyma of the spinal cord.

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.

Understanding the Blood-Brain Barrier and Beyond: Challenges and Opportunities for Novel CNS Therapeutics

This review addresses questions on how to accomplish successful central nervous system (CNS) drug delivery (i.e., having the right concentration at the right CNS site, at the right time), by understanding the rate and extent of blood‐brain barrier (BBB) transport and intra‐CNS distribution in relation to CNS target site(s) exposure. To this end, we need to obtain and integrate quantitative and connected data on BBB using the Combinatory Mapping Approach that includes in vivo and ex vivo animal measurements, and the physiologically based comprehensive LEICNSPK3.0 mathematical model that can translate from animals to humans. For small molecules, slow diffusional BBB transport and active influx and efflux BBB transport determine the differences between plasma and CNS pharmacokinetics. Obviously, active efflux is important for limiting CNS drug delivery. Furthermore, liposomal formulations of small molecules may to a certain extent circumvent active influx and efflux at the BBB. Interestingly, for CNS pathologies, despite all reported disease associated BBB and CNS functional changes in animals and humans, integrative studies typically show a lack of changes on CNS drug delivery for the small molecules. In contrast, the understanding of the complex vesicle‐based BBB transport modes that are important for CNS delivery of large molecules is in progress, and their BBB transport seems to be significantly affected by CNS diseases. In conclusion, today, CNS drug delivery of small drugs can be well assessed and understood by integrative approaches, although there is still quite a long way to go to understand CNS drug delivery of large molecules.

Targeting Transporters for Drug Delivery to the Brain: Can We Do Better?

Limited drug delivery to the brain is one of the major reasons for high failure rates of central nervous system (CNS) drug candidates. The blood–brain barrier (BBB) with its tight junctions, membrane transporters, receptors and metabolizing enzymes is a main player in drug delivery to the brain, restricting the entrance of the drugs and other xenobiotics. Current knowledge about the uptake transporters expressed at the BBB and brain parenchymal cells has been used for delivery of CNS drugs to the brain via targeting transporters. Although many transporter-utilizing (pro)drugs and nanocarriers have been developed to improve the uptake of drugs to the brain, their success rate of translation from preclinical development to humans is negligible. In the present review, we provide a systematic summary of the current progress in development of transporter-utilizing (pro)drugs and nanocarriers for delivery of drugs to the brain. In addition, we applied CNS pharmacokinetic concepts for evaluation of the limitations and gaps in investigation of the developed transporter-utilizing (pro)drugs and nanocarriers. Finally, we give recommendations for a rational development of transporter-utilizing drug delivery systems targeting the brain based on CNS pharmacokinetic principles.

In Vitro-In Vivo Correlation of Blood-Brain Barrier Permeability of Drugs: A Feasibility Study Towards Development of Prediction Methods for Brain Drug Concentration in Humans

PURPOSE

In vitro human blood-brain barrier (BBB) models in combination with central nervous system-physiologically based pharmacokinetic (CNS-PBPK) modeling, hereafter referred to as the "BBB/PBPK" method, are expected to contribute to prediction of brain drug concentration profiles in humans. As part of our ongoing effort to develop a BBB/PBPK method, we tried to clarify the relationship of in vivo BBB permeability data to those in vitro obtained from a human immortalized cell-based tri-culture BBB model (hiBBB), which we have recently created.

METHODS

The hiBBB models were developed and functionally characterized as previously described. The in vitro BBB permeabilities (Pe, × 10^-6 cm/s) of seventeen compounds were determined by permeability assays, and in vivo BBB permeabilities (Q_ECF) for eight drugs were estimated by CNS-PBPK modeling. The correlation of the Pe values with the Q_ECF values was analyzed by linear regression analysis.

RESULTS

The hiBBB models showed intercellular barrier properties and several BBB transporter functions, which were enough to provide a wide dynamic range of Pe values from 5.7 ± 0.7 (rhodamine 123) to 2580.4 ± 781.9 (rivastigmine). Furthermore, the in vitro Pe values of the eight drugs showed a good correlation (R² = 0.96) with their in vivo Q_ECF values estimated from human clinical data.

CONCLUSION

We show that in vitro human BBB models provide clinically relevant BBB permeability that can be used as input for CNS-PBPK modeling. Therefore, our findings will encourage the development of a BBB/PBPK method as a promising approach for predicting brain drug concentration profiles in humans.

Free Drug Theory - No Longer Just a Hypothesis?

The Free Drug Hypothesis is a well-established concept within the scientific lexicon pervading many areas of Drug Discovery and Development, and yet it is poorly defined by virtue of many variations appearing in the literature. Clearly, unbound drug is in dynamic equilibrium with respect to absorption, distribution, metabolism, elimination, and indeed, interaction with the desired pharmacological target. Binding interactions be they specific (e.g. high affinity) or nonspecific (e.g. lower affinity/higher capacity) are governed by the same fundamental physicochemical tenets including Hill-Langmuir Isotherms, the Law of Mass Action and Drug Receptor Theory. With this in mind, it is time to recognise a more coherent version and consider it the Free Drug Theory and a hypothesis no longer. Today, we have the experimental and modelling capabilities, pharmacological knowledge, and an improved understanding of unbound drug distribution (e.g. Kp_uu) to raise the bar on our understanding and analysis of experimental data. The burden of proof should be to rule out mechanistic possibilities and/or experimental error before jumping to the conclusion that any observations contradict these fundamentals.

In vitro blood brain barrier models: An overview

Understanding the composition and function of the blood brain barrier (BBB) enables the development of novel, innovative techniques for administering central nervous system (CNS) medications and technologies for improving the existing models. Scientific and methodological interest in the pathology of the BBB resulted in the formation of numerous in vitro BBB models. Once successfully studied and modelled, it would be a valuable tool for elucidating the mechanism of action of the CNS disorders prior to their manifestation and the pathogenic factors. Understanding the rationale behind the selection of the models as well as their working may enable the development of state-of-the-art drugs for treating and managing neurological diseases. Hence, to have realistic simulation of the BBB and test its drug permeability the microfluidics-based BBB-on-Chip model has been developed. To summarise, we aim to evaluate the advanced, newly developed and frequently used in vitro BBB models, thereby providing a brief overview of the components essential for in vitro BBB formation, the methods of chip fabrication and cell culturing, its applications and the recent advances in this technological field. This will be critical for developing CNS treatments with improved BBB penetrability and pharmacokinetic properties.

DNA damage repair in glioblastoma: current perspectives on its role in tumour progression, treatment resistance and PIKKing potential therapeutic targets

BACKGROUND

The aggressive, invasive and treatment resistant nature of glioblastoma makes it one of the most lethal cancers in humans. Total surgical resection is difficult, and a combination of radiation and chemotherapy is used to treat the remaining invasive cells beyond the tumour border by inducing DNA damage and activating cell death pathways in glioblastoma cells. Unfortunately, recurrence is common and a major hurdle in treatment, often met with a more aggressive and treatment resistant tumour. A mechanism of resistance is the response of DNA repair pathways upon treatment-induced DNA damage, which enact cell-cycle arrest and repair of DNA damage that would otherwise cause cell death in tumour cells.

CONCLUSIONS

In this review, we discuss the significance of DNA repair mechanisms in tumour formation, aggression and treatment resistance. We identify an underlying trend in the literature, wherein alterations in DNA repair pathways facilitate glioma progression, while established high-grade gliomas benefit from constitutively active DNA repair pathways in the repair of treatment-induced DNA damage. We also consider the clinical feasibility of inhibiting DNA repair in glioblastoma and current strategies of using DNA repair inhibitors as agents in combination with chemotherapy, radiation or immunotherapy. Finally, the importance of blood-brain barrier penetrance when designing novel small-molecule inhibitors is discussed.

Brain Metastasis Treatment: The Place of Tyrosine Kinase Inhibitors and How to Facilitate Their Diffusion across the Blood-Brain Barrier

The incidence of brain metastases has been increasing constantly for the last 20 years, because of better control of metastases outside the brain, and the failure of most drugs to cross the blood–brain barrier at relevant pharmacological concentrations. Recent advances in the molecular biology of cancer have led to the identification of numerous molecular alterations, some of them targetable with the development of specific targeted therapies, including tyrosine kinase inhibitors. In this narrative review, we set out to describe the state-of-the-art in the use of tyrosine kinase inhibitors for the treatment of melanoma, lung cancer, and breast cancer brain metastases. We also report preclinical and clinical pharmacological data on brain exposure to tyrosine kinase inhibitors after oral administration and describe the most recent advances liable to facilitate their penetration of the blood–brain barrier at relevant concentrations and limit their physiological efflux.

Application of in vitro data in physiologically-based kinetic models for quantitative in vitro-in vivo extrapolation: A case-study for baclofen

Physiologically-based kinetic (PBK) models can simulate concentrations of chemicals in tissues over time without animal experiments. Nevertheless, in vivo data are often used to parameterise PBK models. This study aims to illustrate that a combination of kinetic and dynamic readouts from in vitro assays can be used to parameterise PBK models simulating neurologically-active concentrations of xenobiotics. Baclofen, an intrathecally administered drug to treat spasticity, was used as a proof-of-principle xenobiotic. An in vitro blood-brain barrier (BBB) model was used to determine the BBB permeability of baclofen needed to simulate plasma and cerebrospinal concentrations. Simulated baclofen concentrations in individuals and populations of adults and children generally fall within 2-fold of measured clinical study concentrations. Further, in vitro micro-electrode array recordings were used to determine the effect of baclofen on neuronal activity (cell signalling). Using quantitative in vitro-in vivo extrapolations (QIVIVE) corresponding doses of baclofen were estimated. QIVIVE showed that up to 4600 times lower intrathecal doses than oral and intravenous doses induce comparable neurological effects. Most simulated doses were in the range of administered doses. This show that PBK models predict concentrations in the central nervous system for various routes of administration accurately without the need for additional in vivo data.

A helicase-primase drug candidate with sufficient target tissue exposure affects latent neural herpes simplex virus infections

More than 50% of the world population is chronically infected with herpesviruses. Herpes simplex virus (HSV) infections are the cause of herpes labialis (cold sores), genital herpes, and sight-impairing keratitis. Less frequently, life-threatening disseminated disease (encephalitis and generalized viremia) can also occur, mainly in immunocompromised patients and newborns. After primary infection, HSV persists for life in a latent state in trigeminal or sacral ganglia and, triggered by diverse stimuli, disease recurs in more than 30% of patients up to several times a year. Current therapy with nucleoside analogs targeting the viral polymerase is somewhat effective but limited by poor exposure in the nervous system, and latent infections are not affected by therapy. Here, we report on an inhibitor of HSV helicase-primase with potent in vitro anti-herpes activity, a different mechanism of action, a low frequency of HSV resistance, and a favorable pharmacokinetic and safety profile. Improved target tissue exposure results in superior efficacy in preventing and treating HSV infection and disease in animal models as compared to standard of care. Therapy of primary HSV infections with drug candidate IM-250 {(S)-2-(2',5'-difluoro-[1,1'-biphenyl]-4-yl)-N-methyl-N-(4-methyl-5-(S-methylsulfon-imidoyl)thiazol-2-yl)acetamide} not only reduces the duration of disease symptoms or time to healing but also prevents recurrent disease in guinea pigs. Treatment of recurrent infections reduces the frequency of recurrences and viral shedding, and, unlike nucleosidic drugs, IM-250 remains effective for a time after cessation of treatment. Hence, IM-250 has advantages over standard-of-care therapies and represents a promising therapeutic for chronic HSV infection, including nucleoside-resistant HSV.

Bioengineered microfluidic blood-brain barrier models in oncology research

Metastasis is the major reason for most brain tumors with up to a 50% chance of occurrence in patients with other types of malignancies. Brain metastasis occurs if cancer cells succeed to cross the 'blood-brain barrier' (BBB). Moreover, changes in the structure and function of BBB can lead to the onset and progression of diseases including neurological disorders and brain-metastases. Generating BBB models with structural and functional features of intact BBB is highly important to better understand the molecular mechanism of such ailments and finding novel therapeutic agents targeting them. Hence, researchers are developing novel in vitro BBB platforms that can recapitulate the structural and functional characteristics of BBB. Brain endothelial cells-based in vitro BBB models have thus been developed to investigate the mechanism of brain metastasis through BBB and facilitate the testing of brain targeted anticancer drugs. Bioengineered constructs integrated with microfluidic platforms are vital tools for recapitulating the features of BBB in vitro closely as possible. In this review, we outline the fundamentals of BBB biology, recent developments in the microfluidic BBB platforms, and provide a concise discussion of diverse types of bioengineered BBB models with an emphasis on the application of them in brain metastasis and cancer research in general. We also provide insights into the challenges and prospects of the current bioengineered microfluidic platforms in cancer research.

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.

Miniaturization and Automation of a Human In Vitro Blood-Brain Barrier Model for the High-Throughput Screening of Compounds in the Early Stage of Drug Discovery

Central nervous system (CNS) diseases are one of the top causes of death worldwide. As there is a difficulty of drug penetration into the brain due to the blood–brain barrier (BBB), many CNS drugs treatments fail in clinical trials. Hence, there is a need to develop effective CNS drugs following strategies for delivery to the brain by better selecting them as early as possible during the drug discovery process. The use of in vitro BBB models has proved useful to evaluate the impact of drugs/compounds toxicity, BBB permeation rates and molecular transport mechanisms within the brain cells in academic research and early-stage drug discovery. However, these studies that require biological material (animal brain or human cells) are time-consuming and involve costly amounts of materials and plastic wastes due to the format of the models. Hence, to adapt to the high yields needed in early-stage drug discoveries for compound screenings, a patented well-established human in vitro BBB model was miniaturized and automated into a 96-well format. This replicate met all the BBB model reliability criteria to get predictive results, allowing a significant reduction in biological materials, waste and a higher screening capacity for being extensively used during early-stage drug discovery studies.

Translational CNS Steady-State Drug Disposition Model in Rats, Monkeys, and Humans for Quantitative Prediction of Brain-to-Plasma and Cerebrospinal Fluid-to-Plasma Unbound Concentration Ratios

Capturing unbound drug exposure in the brain is crucial to evaluate pharmacological effects for drugs acting on the central nervous system. However, to date, there are no reports of validated prediction models to determine the brain-to-plasma unbound concentration ratio (K_p,uu,brain) as well as the cerebrospinal fluid (CSF)-to-plasma unbound concentration ratio (K_p,uu,CSF) between humans and other species. Here, we developed a translational CNS steady-state drug disposition model to predict K_p,uu,brain and K_p,uu,CSF across rats, monkeys, and humans by estimating the relative activity factors (RAF) for MDR1 and BCRP in addition to scaling factors (γ and σ) using the molecular weight, logD, CSF bulk flow, and in vitro transport activities of these transporters. In this study, 68, 26, and 28 compounds were tested in the rat, monkey, and human models, respectively. Both the predicted K_p,uu,brain and K_p,uu,CSF values were within the 3-fold range of the observed values (71, 73, and 79%; 79, 88, and 78% of the compounds, respectively), indicating successful prediction of K_p,uu,brain and K_p,uu,CSF in the three species. The overall predictivity of the RAF approach is consistent with that of the relative expression factor (REF) approach. As the established model can predict K_p,uu,brain and K_p,uu,CSF using only in vitro and physicochemical data, this model would help avoid ethical issues related to animal use and improve CNS drug discovery workflow.

Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model

The mechanistic neuropharmacokinetic (neuroPK) model was established to predict unbound brain-to-plasma partitioning (K_p,uu,brain) by considering in vitro efflux activities of multiple drug resistance 1 (MDR1) and breast cancer resistance protein (BCRP). Herein, we directly compare this model to a computational machine learning approach utilizing physicochemical descriptors and efflux ratios of MDR1 and BCRP-expressing cells for predicting K_p,uu,brain in rats. Two different types of machine learning techniques, Gaussian processes (GP) and random forest regression (RF), were assessed by the time and cluster-split validation methods using 640 internal compounds. The predictivity of machine learning models based on only molecular descriptors in the time-split dataset performed worse than the cluster-split dataset, whereas the models incorporating MDR1 and BCRP efflux ratios showed similar predictivity between time and cluster-split datasets. The GP incorporating MDR1 and BCRP in the time-split dataset achieved the highest correlation (R² = 0.602). These results suggested that incorporation of MDR1 and BCRP in machine learning is beneficial for robust and accurate prediction. K_p,uu,brain prediction utilizing the neuroPK model was significantly worse compared to machine learning approaches for the same dataset. We also investigated the predictivity of K_p,uu,brain using an external independent test set of 34 marketed drugs. Compared to machine learning models, the neuroPK model showed better predictive performance with R² of 0.577. This work demonstrates that the machine learning model for K_p,uu,brain achieves maximum predictive performance within the chemical applicability domain, whereas the neuroPK model is applicable more widely beyond the chemical space covered in the training dataset.

In vitro model for predicting the access and distribution of drugs in the brain using hCMEC/D3 cells

The BBB is a protective entity that prevents external substances from reaching the CNS but it also hinders the delivery of drugs into the brain when they are needed. The main objective of this work was to improve a previously proposed in vitro cell-based model by using a more physiological cell line (hCMEC/D3) to predict the main pharmacokinetic parameters that describe the access and distribution of drugs in the CNS: Kp_uu,brain, f_u,plasma, f_u,brain and V_u,brain. The hCMEC/D3 permeability of seven drugs was studied in transwell systems under different conditions (standard, modified with albumin and modified with brain homogenate). From the permeability coefficients of those experiments, the parameters mentioned above were calculated and four linear IVIVCs were established. The best ones were those that relate the in vitro and in vivo V_u,brain and f_u,brain (r² = 0.961 and r² = 0.940) which represent the binding rate of a substance to the brain tissue, evidencing the importance of using brain homogenate to mimic brain tissue when an in vitro brain permeability assay is done. This methodology could be a high-throughput screening tool in drug development to select the CNS promising drugs in three different in vitro BBB models (hCMEC/D3, MDCK and MDCK-MDR1).

Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration

Developing in silico models to predict the brain penetration of drugs remains a challenge owing to the intricate involvement of multiple transport systems in the blood brain barrier, and the necessity to consider a combination of multiple pharmacokinetic parameters. P-glycoprotein (P-gp) is one of the most important transporters affecting the brain penetration of drugs. Here, we developed an in silico prediction model for P-gp efflux potential in brain capillary endothelial cells (BCEC). Using the representative values of P-gp net efflux ratio in BCEC, we proposed a novel prediction system for brain-to-plasma concentration ratio (Kp,brain) and unbound brain-to-plasma concentration ratio (Kp,uu,brain) of P-gp substrates. We validated the proposed prediction system using newly acquired experimental brain penetration data of 28 P-gp substrates. Our system improved the predictive accuracy of brain penetration of drugs using only chemical structure information compared with that of previous studies.

Treating viruses in the brain: Perspectives from NeuroAIDS

Aggressive use of antiretroviral therapy has led to excellent viral suppression within the systemic circulation. However, despite these advances, HIV reservoirs still persist. The persistence of HIV within the brain can lead to the development of HIV-associated neurocognitive disorders (HAND). Although the causes of the development of neurocognitive disorders is likely multifactorial, the inability of antiretroviral therapy to achieve adequate concentrations within the brain is likely a major contributing factor. Information about antiretroviral drug exposure within the brain is limited. Clinically, drug concentrations within the cerebrospinal fluid (CSF) are used as markers for central nervous system (CNS) drug exposure. However, significant differences exist; CSF concentration is often a poor predictor of drug exposure within the brain. This article reviews the current information regarding antiretroviral exposure within the brain in humans as well as preclinical animals and discusses the impact of co-morbidities on antiretroviral efficacy within the brain. A more thorough understanding of antiretroviral penetration into the brain is an essential component to the development of better therapeutic strategies for neuroAIDS.

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.

Neuropharmacokinetic visualization of regional and subregional unbound antipsychotic drug transport across the blood-brain barrier

Comprehensive determination of the extent of drug transport across the region-specific blood-brain barrier (BBB) is a major challenge in preclinical studies. Multiple approaches are needed to determine the regional free (unbound) drug concentration at which a drug engages with its therapeutic target. We present an approach that merges in vivo and in vitro neuropharmacokinetic investigations with mass spectrometry imaging to quantify and visualize both the extent of unbound drug BBB transport and the post-BBB cerebral distribution of drugs at regional and subregional levels. Direct imaging of the antipsychotic drugs risperidone, clozapine, and olanzapine using this approach enabled differentiation of regional and subregional BBB transport characteristics at 20-µm resolution in small brain regions, which could not be achieved by other means. Our approach allows investigation of heterogeneity in BBB transport and presents new possibilities for molecular psychiatrists by facilitating interpretation of regional target-site exposure results and decision-making.

In Situ Surface Modification of Microfluidic Blood-Brain-Barriers for Improved Screening of Small Molecules and Nanoparticles

Here, we have developed and evaluated a microfluidic-based human blood-brain-barrier (μBBB) platform that models and predicts brain tissue uptake of small molecule drugs and nanoparticles (NPs) targeting the central nervous system. By using a photocrosslinkable copolymer that was prepared from monomers containing benzophenone and N-hydroxysuccinimide ester functional groups, we were able to evenly coat and functionalize μBBB chip channels in situ, providing a covalently attached homogenous layer of extracellular matrix proteins. This novel approach allowed the coculture of human endothelial cells, pericytes, and astrocytes and resulted in the formation of a mimic of cerebral endothelium expressing tight junction markers and efflux proteins, resembling the native BBB. The permeability coefficients of a number of compounds, including caffeine, nitrofurantoin, dextran, sucrose, glucose, and alanine, were measured on our μBBB platform and were found to agree with reported values. In addition, we successfully visualized the receptor-mediated uptake and transcytosis of transferrin-functionalized NPs. The BBB-penetrating NPs were able to target glioma cells cultured in 3D in the brain compartment of our μBBB. In conclusion, our μBBB was able to accurately predict the BBB permeability of both small molecule pharmaceuticals and nanovectors and allowed time-resolved visualization of transcytosis. Our versatile chip design accommodates different brain disease models and is expected to be exploited in further BBB studies, aiming at replacing animal experiments.

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.

Use of Intravenous Infusion Study Design to Simultaneously Determine Brain Penetration and Systemic Pharmacokinetic Parameters in Rats

In drug discovery, the extent of brain penetration as measured by free brain/plasma concentration ratio (K_p,uu) is normally determined from one experiment after constant intravenous infusion, and pharmacokinetics (PK) parameters, including clearance (CL), volume of distribution at steady state (Vss), and effective half-life (t _{1/2 ,eff}) are determined from another experiment after a single intravenous bolus injection. The objective of the present study was to develop and verify a method to simultaneously determine K_p,uu and PK parameters from a single intravenous infusion experiment. In this study, nine compounds (atenolol, loperamide, minoxidil, N-[3-(4'-fluorophenyl)-3-(4'-phenylphenoxy)propyl]sarcosine, sulpiride, and four proprietary compounds) were intravenously infused for 4 hours at 1 mg/kg or 24 hours at 1 or 6 mg/kg or bolus injected at 1 mg/kg. Plasma samples were serially collected, and brain and cerebrospinal fluid samples were collected at the end of infusion. The PK parameters were obtained using noncompartmental analysis (NCA) and compartmental analysis. The K_p,uu,brain values of those compounds increased up to 2.86-fold from 4 to 24 hours. The CL calculated from infusion rate over steady-state concentration from the 24-hour infusion studies was more consistent with the CL from the intravenous bolus studies than that from 4-hour infusion studies (CL avg. fold of difference 1.19-1.44 vs. 2.10). The compartmental analysis using one- and two-compartment models demonstrated better performance than NCA regardless of study design. In addition, volume of distribution at steady state and t _1/2,eff could be accurately obtained by one-compartment analysis within 2-fold difference. In conclusion, both unbound brain-to-plasma ratio and PK parameters can be successfully estimated from a 24-hour intravenous infusion study design. SIGNIFICANCE STATEMENT: We demonstrated that the extent of brain penetration and pharmacokinetic parameters (such as clearance, V_ss, and effective t _1/2) can be determined from a single constant intravenous infusion study in rats.