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

A novel in silico approach for predicting unbound brain-to-plasma ratio using machine learning-based support vector regression

The blood-brain barrier (BBB) functions as a vital protective mechanism, restricting the entry of substances and xenobiotics into the central nervous system (CNS). Consequently, BBB penetration is a critical aspect of absorption, distribution, metabolism, elimination, and toxicity (ADME/Tox) considerations in drug discovery and development as it is essential to minimize CNS-associated side effects in systemically targeted drugs and to enhance efficacy in CNS-targeted therapeutics. In this study, an in silico model utilizing the novel machine learning-based hierarchical support vector regression (HSVR) scheme was developed to predict the unbound brain-to-plasma concentration ratio (Kp,uu,brain) values using a diverse dataset of compounds with known BBB penetration properties. The HSVR model leverages a hierarchical framework to capture the complex relationships between molecular descriptors and BBB penetration mechanisms that can otherwise be insurmountably difficult for traditional methods or other machine learning algorithms. These complexities arise from the fact that BBB penetration can be governed by various factors, including passive diffusion and active influx and efflux transport processes. The accuracy, predictivity, robustness of HSVR were rigorously validated using comprehensive valuation metrics and stringent validation criteria. Its practical application was further substantiated through a mock test. Comparative analyses revealed that the HSVR model outperforms existing published models both quantitatively and qualitatively, providing a reliable tool for early-stage drug discovery and development. The adoption of this model has the potential to significantly streamline BBB penetration assessments, minimizing reliance on in vivo studies while expediting the identification of viable CNS drug candidates or systemic drug candidates prone to CNS-related challenges. This approach aligns seamlessly with the "fail early and fail fast" paradigm of modern drug discovery, enhancing both efficiency and cost-effectiveness.

Assessing central nervous system drug delivery

INTRODUCTION

Delivering drugs to the central nervous system (CNS) remains a major challenge due to the blood-brain barrier, restricting the entry of drugs into the brain. This limitation contributes to the ongoing lack of effective treatments for CNS diseases. To improve the process of drug discovery and development, it is crucial to streamline methods that measure clinically relevant parameters, allowing for good selection of drug candidates.

AREA COVERED

In this paper, we discuss the essential prerequisites for successful CNS drug delivery and review relevant methods. We emphasize the need for closer collaboration between in vitro and in vivo scientists to improve the relevance of these methods and increase the success rate of developing effective CNS therapies. While our focus is on small molecule drugs, we also touch on some aspects of larger molecules.

EXPERT OPINION

Significant progress has been made in recent years in method development and their application. However, there is still work to be done before the use of in silico models, in vitro cell systems, and AI can consistently offer meaningful correlations and relationships to clinical data. This gap is partly due to limited patient data, but a lot can be achieved through in vivo research in animal models.

Reliability of in vitro data for the mechanistic prediction of brain extracellular fluid pharmacokinetics of P-glycoprotein substrates in vivo; are we scaling correctly?

Plasma pharmacokinetic (PK) profiles often do not resemble the PK within the central nervous system (CNS) because of blood-brain-border (BBB) processes, like active efflux by P-glycoprotein (P-gp). Methods to predict CNS-PK are therefore desired. Here we investigate whether in vitro apparent permeability (Papp) and corrected efflux ratio (ERc) extracted from literature can be repurposed as input for the LeiCNS-PK3.4 physiologically-based PK model to confidently predict rat brain extracellular fluid (ECF) PK of P-gp substrates. Literature values of in vitro Caco-2, LLC-PK1-mdr1a/MDR1, and MDCKII-MDR1 cell line transport data were used to calculate P-gp efflux clearance (CLPgp). Subsequently, CLPgp was scaled from in vitro to in vivo through a relative expression factor (REF) based on P-gp expression differences. BrainECF PK was predicted well (within twofold error of the observed data) for 2 out of 4 P-gp substrates after short infusions and 3 out of 4 P-gp substrates after continuous infusions. Variability of in vitro parameters impacted both predicted rate and extent of drug distribution, reducing model applicability. Notably, use of transport data and in vitro P-gp expression obtained from a single study did not guarantee an accurate prediction; it often resulted in worse predictions than when using in vitro expression values reported by other labs. Overall, LeiCNS-PK3.4 shows promise in predicting brainECF PK, but this study highlights that the in vitro to in vivo translation is not yet robust. We conclude that more information is needed about context and drug dependency of in vitro data for robust brainECF PK predictions.

Prediction of the Extent of Blood-Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model

INTRODUCTION

The unbound brain-to-plasma partition coefficient (Kp,uu,BBB) is an essential parameter for predicting central nervous system (CNS) drug disposition using physiologically-based pharmacokinetic (PBPK) modeling. Kp,uu,BBB values for specific compounds are however often unavailable, and are moreover time consuming to obtain experimentally. The aim of this study was to develop a quantitative structure-property relationship (QSPR) model to predict the Kp,uu,BBB and to demonstrate how QSPR-model predictions can be integrated into a physiologically-based pharmacokinetic model for the CNS.

METHODS

Rat Kp,uu,BBB values were obtained for 98 compounds from literature or in house historical data. For all compounds, 2D and 3D physico-chemical and structural properties were derived using the Molecular Operating Environment (MOE) software. Multiple machine learning (ML) regression models were compared for prediction of the Kp,uu,BBB, including random forest, support vector machines, K-nearest neighbors, and (sparse-) partial least squares. Finally, we demonstrate how the developed QSPR model predictions can be integrated into a CNS PBPK modeling workflow.

RESULTS

Among all ML algorithms, a random forest showed the best predictive performance for Kp,uu,BBB on test data with R2 value of 0.61 and 61% of all predictions were within twofold error. The obtained Kp,uu,BBB were successfully integrated into the LeiCNS-PK3.0 CNS PBPK model.

CONCLUSIONS

The developed random forest QSPR model for Kp,uu,BBB prediction was found to have adequate performance, and can support drug discovery and development of novel investigational drugs targeting the CNS in conjunction with CNS PBPK modeling.

Do P-glycoprotein-mediated drug-drug interactions at the blood-brain barrier impact morphine brain distribution?

P-glycoprotein (P-gp) is a key efflux transporter and may be involved in drug-drug interactions (DDIs) at the blood-brain barrier (BBB), which could lead to changes in central nervous system (CNS) drug exposure. Morphine is a P-gp substrate and therefore a potential victim drug for P-gp mediated DDIs. It is however unclear if P-gp inhibitors can induce clinically relevant changes in morphine CNS exposure. Here, we used a physiologically-based pharmacokinetic (PBPK) model-based approach to evaluate the potential impact of DDIs on BBB transport of morphine by clinically relevant P-gp inhibitor drugs.The LeiCNS-PK3.0 PBPK model was used to simulate morphine distribution at the brain extracellular fluid (brainECF) for different clinical intravenous dosing regimens of morphine, alone or in combination with a P-gp inhibitor. We included 34 commonly used P-gp inhibitor drugs, with inhibitory constants and expected clinical P-gp inhibitor concentrations derived from literature. The DDI impact was evaluated by the change in brainECF exposure for morphine alone or in combination with different inhibitors. Our analysis demonstrated that P-gp inhibitors had a negligible effect on morphine brainECF exposure in the majority of simulated population, caused by low P-gp inhibition. Sensitivity analyses showed neither major effects of increasing the inhibitory concentration nor changing the inhibitory constant on morphine brainECF exposure. In conclusion, P-gp mediated DDIs on morphine BBB transport for the evaluated P-gp inhibitors are unlikely to induce meaningful changes in clinically relevant morphine CNS exposure. The developed CNS PBPK modeling approach provides a general approach for evaluating BBB transporter DDIs in humans.

Exploring Kp,uu,BBB values smaller than unity in remoxipride: A physiologically-based CNS model approach highlighting brain metabolism in drugs with passive blood-brain barrier transport

(AIM)

Kp,uu,BBB values are crucial indicators of drug distribution into the brain, representing the steady-state relationship between unbound concentrations in plasma and in brain extracellular fluid (brainECF). Kp,uu,BBB values < 1 are often interpreted as indicators of dominant active efflux transport processes at the blood-brain barrier (BBB). However, the potential impact of brain metabolism on this value is typically not addressed. In this study, we investigated the brain distribution of remoxipride, as a paradigm compound for passive BBB transport with yet unexplained brain elimination that was hypothesized to represent brain metabolism.

(METHODS)

The physiologically-based LeiCNS pharmacokinetic predictor (LeiCNS-PK model) was used to compare brain distribution of remoxipride with and without Michaelis-Menten kinetics at the BBB and/or brain cell organelle levels. To that end, multiple in-house (IV 0.7, 3.5, 4, 5.2, 7, 8, 14 and 16 mg kg-1) and external (IV 4 and 8 mg kg-1) rat microdialysis studies plasma and brainECF data were analysed.

(RESULTS)

The incorporation of active elimination through presumed brain metabolism of remoxipride in the LeiCNS-PK model significantly improved the prediction accuracy of experimentally observed brainECF profiles of this drug. The model integrated with brain metabolism in both barriers and organelles levels is named LeiCNS-PK3.5.

(CONCLUSION)

For drugs with Kp,uu,BBB values < 1, not only the current interpretation of dominant BBB efflux transport, but also potential brain metabolism needs to be considered, especially because these may be concentration dependent. This will improve the mechanistic understanding of the processes that determine brain PK profiles.

Active CNS delivery of oxycodone in healthy and endotoxemic pigs

BACKGROUND

The primary objective of this study was to advance our understanding of active drug uptake at brain barriers in higher species than rodents, by examining oxycodone brain concentrations in pigs.

METHODS

This was investigated by a microdialysis study in healthy and endotoxemic conditions to increase the understanding of inter-species translation of putative proton-coupled organic cation (H+/OC) antiporter-mediated central nervous system (CNS) drug delivery in health and pathology, and facilitate the extrapolation to humans for improved CNS drug treatment in patients. Additionally, we sought to evaluate the efficacy of lumbar cerebrospinal fluid (CSF) exposure readout as a proxy for brain unbound interstitial fluid (ISF) concentrations. By simultaneously monitoring unbound concentrations in blood, the frontal cortical area, the lateral ventricle (LV), and the lumbar intrathecal space in healthy and lipopolysaccharide (LPS)-induced inflammation states within the same animal, we achieved exceptional spatiotemporal resolution in mapping oxycodone transport across CNS barriers.

RESULTS

Our findings provide novel evidence of higher unbound oxycodone concentrations in brain ISF compared to blood, yielding an unbound brain-to-plasma concentration ratio (Kp,uu,brain) of 2.5. This supports the hypothesis of the presence of the H+/OC antiporter system at the blood-brain barrier (BBB) in pigs. Despite significant physiological changes, reflected in pig Sequential Organ Failure Assessment, pSOFA scores, oxycodone blood concentrations and its active net uptake across the BBB remained nearly unchanged during three hours of i.v. infusion of 4 µg/kg/h LPS from Escherichia coli (O111:B4). Mean Kp,uu,LV values indicated active uptake also at the blood-CSF barrier in healthy and endotoxemic pigs. Lumbar CSF concentrations showed minimal inter-individual variability during the experiment, with a mean Kp,uu,lumbarCSF of 1.5. LPS challenge caused a slight decrease in Kp,uu,LV, while Kp,uu,lumbarCSF remained unaffected.

CONCLUSIONS

This study enhances our understanding of oxycodone pharmacokinetics and CNS drug delivery in both healthy and inflamed conditions, providing crucial insights for translating these findings to clinical settings.

Non-linear blood-brain barrier transport and dosing strategies influence receptor occupancy ratios of morphine and its metabolites in pain matrix

BACKGROUND AND PURPOSE

Morphine is important for treatment of acute and chronic pain. However, there is high interpatient variability and often inadequate pain relief and adverse effects. To better understand variability in the dose-effect relationships of morphine, we investigated the effects of its non-linear blood-brain barrier (BBB) transport on μ-receptor occupancy in different CNS locations, in conjunction with its main metabolites that bind to the same receptor.

EXPERIMENTAL APPROACH

CNS exposure profiles for morphine, M3G and M6G for clinically relevant dosing regimens based on intravenous, oral immediate- and extended-release formulations were generated using a physiology-based pharmacokinetic model of the CNS, with non-linear BBB transport of morphine. The simulated CNS exposure profiles were then used to derive corresponding μ-receptor occupancies at multiple CNS pain matrix locations.

KEY RESULTS

Simulated CNS exposure profiles for morphine, M3G and M6G, associated with non-linear BBB transport of morphine resulted in varying μ-receptor occupancies between different dose regimens, formulations and CNS locations. At lower doses, the μ-receptor occupancy of morphine was relatively higher than at higher doses of morphine, due to the relative contribution of M3G and M6G. At such higher doses, M6G showed higher occupancy than morphine, whereas M3G occupancy was low throughout the dose ranges.

CONCLUSION AND IMPLICATIONS

Non-linear BBB transport of morphine affects the μ-receptor occupancy ratios of morphine with its metabolites, depending on dose and route of administration, and CNS location. These predictions need validation in animal or clinical experiments, to understand the clinical implications.

Predictions of Bedaquiline Central Nervous System Exposure in Patients with Tuberculosis Meningitis Using Physiologically based Pharmacokinetic Modeling

BACKGROUND AND OBJECTIVE

The use of bedaquiline as a treatment option for drug-resistant tuberculosis meningitis (TBM) is of interest to address the increased prevalence of resistance to first-line antibiotics. To this end, we describe a whole-body physiologically based pharmacokinetic (PBPK) model for bedaquiline to predict central nervous system (CNS) exposure.

METHODS

A whole-body PBPK model was developed for bedaquiline and its metabolite, M2. The model included compartments for brain and cerebrospinal fluid (CSF). Model predictions were evaluated by comparison to plasma PK time profiles following different dosing regimens and sparse CSF concentrations data from patients. Simulations were then conducted to compare CNS and lung exposures to plasma exposure at clinically relevant dosing schedules.

RESULTS

The model appropriately described the observed plasma and CSF bedaquiline and M2 concentrations from patients with pulmonary tuberculosis (TB). The model predicted a high impact of tissue binding on target site drug concentrations in CNS. Predicted unbound exposures within brain interstitial exposures were comparable with unbound vascular plasma and unbound lung exposures. However, unbound brain intracellular exposures were predicted to be 7% of unbound vascular plasma and unbound lung intracellular exposures.

CONCLUSIONS

The whole-body PBPK model for bedaquiline and M2 predicted unbound concentrations in brain to be significantly lower than the unbound concentrations in the lung at clinically relevant doses. Our findings suggest that bedaquiline may result in relatively inferior efficacy against drug-resistant TBM when compared with efficacy against drug-resistant pulmonary TB.

CNS Viral Infections-What to Consider for Improving Drug Treatment: A Plea for Using Mathematical Modeling Approaches

Neurotropic viruses may cause meningitis, myelitis, encephalitis, or meningoencephalitis. These inflammatory conditions of the central nervous system (CNS) may have serious and devastating consequences if not treated adequately. In this review, we first summarize how neurotropic viruses can enter the CNS by (1) crossing the blood-brain barrier or blood-cerebrospinal fluid barrier; (2) invading the nose via the olfactory route; or (3) invading the peripheral nervous system. Neurotropic viruses may then enter the intracellular space of brain cells via endocytosis and/or membrane fusion. Antiviral drugs are currently used for different viral CNS infections, even though their use and dosing regimens within the CNS, with the exception of acyclovir, are minimally supported by clinical evidence. We therefore provide considerations to optimize drug treatment(s) for these neurotropic viruses. Antiviral drugs should cross the blood-brain barrier/blood cerebrospinal fluid barrier and pass the brain cellular membrane to inhibit these viruses inside the brain cells. Some antiviral drugs may also require intracellular conversion into their active metabolite(s). This illustrates the need to better understand these mechanisms because these processes dictate drug exposure within the CNS that ultimately determine the success of antiviral drugs for CNS infections. Finally, we discuss mathematical model-based approaches for optimizing antiviral treatments. Thereby emphasizing the potential of CNS physiologically based pharmacokinetic models because direct measurement of brain intracellular exposure in living humans faces ethical restrictions. Existing physiologically based pharmacokinetic models combined with in vitro pharmacokinetic/pharmacodynamic information can be used to predict drug exposure and evaluate efficacy of antiviral drugs within the CNS, to ultimately optimize the treatments of CNS viral infections.

Interspecies Brain PBPK Modeling Platform to Predict Passive Transport through the Blood-Brain Barrier and Assess Target Site Disposition

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood-brain barrier (BBB) permeability evaluation tools are needed to explore drugs' ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.

The potential impact of CYP and UGT drug-metabolizing enzymes on brain target site drug exposure

Drug metabolism is one of the critical determinants of drug disposition throughout the body. While traditionally associated with the liver, recent research has unveiled the presence and functional significance of drug-metabolizing enzymes (DMEs) within the brain. Specifically, cytochrome P-450 enzymes (CYPs) and UDP-glucuronosyltransferases (UGTs) enzymes have emerged as key players in drug biotransformation within the central nervous system (CNS). This comprehensive review explores the cellular and subcellular distribution of CYPs and UGTs within the CNS, emphasizing regional expression and contrasting profiles between the liver and brain, humans and rats. Moreover, we discuss the impact of species and sex differences on CYPs and UGTs within the CNS. This review also provides an overview of methodologies for identifying and quantifying enzyme activities in the brain. Additionally, we present factors influencing CYPs and UGTs activities in the brain, including genetic polymorphisms, physiological variables, pathophysiological conditions, and environmental factors. Examples of CYP- and UGT-mediated drug metabolism within the brain are presented at the end, illustrating the pivotal role of these enzymes in drug therapy and potential toxicity. In conclusion, this review enhances our understanding of drug metabolism's significance in the brain, with a specific focus on CYPs and UGTs. Insights into the expression, activity, and influential factors of these enzymes within the CNS have crucial implications for drug development, the design of safe drug treatment strategies, and the comprehension of drug actions within the CNS. To that end, CNS pharmacokinetic (PK) models can be improved to further advance drug development and personalized therapy.

Using the LeiCNS-PK3.0 Physiologically-Based Pharmacokinetic Model to Predict Brain Extracellular Fluid Pharmacokinetics in Mice

INTRODUCTION

The unbound brain extracelullar fluid (brainECF) to plasma steady state partition coefficient, Kp,uu,BBB, values provide steady-state information on the extent of blood-brain barrier (BBB) transport equilibration, but not on pharmacokinetic (PK) profiles seen by the brain targets. Mouse models are frequently used to study brain PK, but this information cannot directly be used to inform on human brain PK, given the different CNS physiology of mouse and human. Physiologically based PK (PBPK) models are useful to translate PK information across species.

AIM

Use the LeiCNS-PK3.0 PBPK model, to predict brain extracellular fluid PK in mice.

METHODS

Information on mouse brain physiology was collected from literature. All available connected data on unbound plasma, brainECF PK of 10 drugs (cyclophosphamide, quinidine, erlotonib, phenobarbital, colchicine, ribociclib, topotecan, cefradroxil, prexasertib, and methotrexate) from different mouse strains were used. Dosing regimen dependent plasma PK was modelled, and Kpuu,BBB values were estimated, and provided as input into the LeiCNS-PK3.0 model to result in prediction of PK profiles in brainECF.

RESULTS

Overall, the model gave an adequate prediction of the brainECF PK profile for 7 out of the 10 drugs. For 7 drugs, the predicted versus observed brainECF data was within two-fold error limit and the other 2 drugs were within five-fold error limit.

CONCLUSION

The current version of the mouse LeiCNS-PK3.0 model seems to reasonably predict available information on brainECF from healthy mice for most drugs. This brings the translation between mouse and human brain PK one step further.

Active Uptake of Oxycodone at Both the Blood-Cerebrospinal Fluid Barrier and The Blood-Brain Barrier without Sex Differences: A Rat Microdialysis Study

BACKGROUND

Oxycodone active uptake across the blood-brain barrier (BBB) is associated with the putative proton-coupled organic cation (H+/OC) antiporter system. Yet, the activity of this system at the blood-cerebrospinal fluid barrier (BCSFB) is not fully understood. Additionally, sex differences in systemic pharmacokinetics and pharmacodynamics of oxycodone has been reported, but whether the previous observations involve sex differences in the function of the H+/OC antiporter system remain unknown. The objective of this study was, therefore, to investigate the extent of oxycodone transport across the BBB and the BCSFB in female and male Sprague-Dawley rats using microdialysis.

METHODS

Microdialysis probes were implanted in the blood and two of the following brain locations: striatum and lateral ventricle or cisterna magna. Oxycodone was administered as an intravenous infusion, and dialysate, blood and brain were collected. Unbound partition coefficients (Kp,uu) were calculated to understand the extent of oxycodone transport across the blood-brain barriers. Non-compartmental analysis was conducted using Phoenix 64 WinNonlin. GraphPad Prism version 9.0.0 was used to perform t-tests, one-way and two-way analysis of variance followed by Tukey's or Šídák's multiple comparison tests. Differences were considered significant at p < 0.05.

RESULTS

The extent of transport at the BBB measured in striatum was 4.44 ± 1.02 (Kp,uu,STR), in the lateral ventricle 3.41 ± 0.74 (Kp,uu,LV) and in cisterna magna 2.68 ± 1.01 (Kp,uu,CM). These Kp,uu values indicate that the extent of oxycodone transport is significantly lower at the BCSFB compared with that at the BBB, but still confirm the presence of active uptake at both blood-brain interfaces. No significant sex differences were observed in neither the extent of oxycodone delivery to the brain, nor in the systemic pharmacokinetics of oxycodone.

CONCLUSIONS

The findings clearly show that active uptake is present at both the BCSFB and the BBB. Despite some underestimation of the extent of oxycodone delivery to the brain, CSF may be an acceptable surrogate of brain ISF for oxycodone, and potentially also other drugs actively transported into the brain via the H+/OC antiporter system.

Does nonlinear blood-brain barrier transport matter for (lower) morphine dosing strategies?

Morphine blood-brain barrier (BBB) transport is governed by passive diffusion, active efflux and saturable active influx. This may result in nonlinear plasma concentration-dependent brain extracellular fluid (brainECF) pharmacokinetics of morphine. In this study, we aim to evaluate the impact of nonlinear BBB transport on brainECF pharmacokinetics of morphine and its metabolites for different dosing strategies using a physiologically based pharmacokinetic simulation study. We extended the human physiologically based pharmacokinetic LeiCNS-PK3.0, model with equations for nonlinear BBB transport of morphine. Simulations for brainECF pharmacokinetics were performed for various dosing strategies: intravenous (IV), oral immediate (IR) and extended release (ER) with dose range of 0.25-150 mg and dosing frequencies of 1-6 times daily. The impact of nonlinear BBB transport on morphine CNS pharmacokinetics was evaluated by quantifying (i) the relative brainECF to plasma exposure (AUCu,brainECF/AUCu,plasma) and (ii) the impact on the peak-to-trough ratio (PTR) of concentration-time profiles in brainECF and plasma. We found that the relative morphine exposure and PTRs are dose dependent for the evaluated dose range. The highest relative morphine exposure value of 1.4 was found for once daily 0.25 mg ER and lowest of 0.1 for 6-daily 150 mg IV dosing. At lower doses the PTRs were smaller and increased with increasing dose and stabilized at higher doses independent of dosing frequency. Relative peak concentrations of morphine in relation to its metabolites changed with increasing dose. We conclude that nonlinearity of morphine BBB transport affects the relative brainECF exposure and the fluctuation of morphine and its metabolites mainly at lower dosing regimens.

Blood-brain barrier perturbations by uremic toxins: key contributors in chronic kidney disease-induced neurological disorders?

Chronic kidney disease is multifactorial and estimated to affect more than 840 million people worldwide constituting a major global health crisis. The number of patients will continue to rise mostly because of the ageing population and the increased prevalence of comorbidities such as diabetes and hypertension. Patients with advanced stages display a loss of kidney function leading to an accumulation of, a.o. protein-bound uremic toxins that are poorly eliminated by renal replacement therapies. This systemic retention of toxic metabolites, known as the uremic syndrome, affects other organs. Indeed, neurological complications such as cognitive impairment, uremic encephalopathy, and anxiety have been reported in chronic kidney disease patients. Several factors are involved, including hemodynamic disorders and blood-brain barrier (BBB) impairment. The BBB guarantees the exchange of solutes between the blood and the brain through a complex cellular organization and a diverse range of transport proteins. We hypothesize that the increased exposure of the brain to protein-bound uremic toxins is involved in BBB disruption and induces a perturbation in the activity of endothelial membrane transporters. This phenomenon could play a part in the evolution of neurological disorders driven by this kidney-brain crosstalk impairment. In this review, we present chronic kidney disease-induced neurological complications by focusing on the pathological relationship between the BBB and protein-bound uremic toxins. The importance of mechanistically delineating the impact of protein-bound uremic toxins on BBB integrity and membrane drug transporter expression and function in brain endothelial capillary cells is highlighted. Additionally, we put forward current knowledge gaps in the literature.

Population Pharmacokinetic Analysis of Rifampicin in Plasma, Cerebrospinal Fluid, and Brain Extracellular Fluid in South African Children with Tuberculous Meningitis

Limited knowledge is available on the pharmacokinetics of rifampicin in children with tuberculous meningitis (TBM) and its penetration into brain tissue, which is the site of infection. In this analysis, we characterize the distribution of rifampicin in cerebrospinal fluid (CSF), lumbar (LCSF) and ventricular (VCSF), and brain extracellular fluid (ECF). Children with TBM were included in this pharmacokinetic analysis. Sparse plasma, LCSF, and VCSF samples were collected opportunistically, as clinically indicated. Brain ECF was sampled using microdialysis (MD). Rifampicin was quantified with liquid chromatography with tandem mass spectrometry in all samples, and 25-desacetyl rifampicin in the plasma samples. The data were interpreted with nonlinear mixed-effects modeling, with the CSF and brain ECF modeled as "effect compartments." Data were available from 61 children, with median (min-max) age of 2 (0.3 to 10) years and weight of 11.0 (4.8 to 49.0) kg. A one-compartment model for parent and metabolite with first-order absorption and elimination via saturable hepatic clearance described the data well. Allometric scaling, maturation, and auto-induction of clearance were included. The pseudopartition coefficient between plasma and LCSF/VCSF was ~5%, while the value for ECF was only ~0.5%, possibly reflecting low recovery of rifampicin using MD. The equilibration half-life between plasma and LCSF/VCSF was ~4 h and between plasma and ECF ~2 h. Our study confirms previous reports showing that rifampicin concentrations in the LCSF are lower than in plasma and provides novel knowledge about rifampicin in the VCSF and the brain tissue. Despite MD being semiquantitative because the relative recovery cannot be quantified, our study presents a proof-of-concept that rifampicin reaches the brain tissue and that MD is an attractive technique to study site-of-disease pharmacokinetics in TBM.

The PBPK LeiCNS-PK3.0 framework predicts Nirmatrelvir (but not Remdesivir or Molnupiravir) to achieve effective concentrations against SARS-CoV-2 in human brain cells

SARS-CoV-2 was shown to infect and persist in the human brain cells for up to 230 days, highlighting the need to treat the brain viral load. The CNS disposition of the antiCOVID-19 drugs: Remdesivir, Molnupiravir, and Nirmatrelvir, remains, however, unexplored. Here, we assessed the human brain pharmacokinetic profile (PK) against the EC₉₀ values of the antiCOVID-19 drugs to predict drugs with favorable brain PK against the delta and the omicron variants. We also evaluated the intracellular PK of GS443902 and EIDD2061, the active metabolites of Remdesivir and Molnupiravir, respectively. Towards this, we applied LeiCNS-PK3.0, the physiologically based pharmacokinetic framework with demonstrated adequate predictions of human CNS PK. Under the recommended dosing regimens, the predicted brain extracellular fluid PK of only Nirmatrelvir was above the variants' EC₉₀. The intracellular levels of GS443902 and EIDD2061 were below the intracellular EC₉₀. Summarizing, our model recommends Nirmatrelvir as the promising candidate for (pre)clinical studies investigating the CNS efficacy of antiCOVID-19 drugs.

A Physiologically-Based Pharmacokinetic Model of the Brain Considering Regional Lipid Variance

Modeling and simulation of the central nervous system provides a tool for understanding and predicting the distribution of small molecules throughout the brain tissue and cerebral spinal fluid (CSF), and these efforts often rely on empirical data to make predictions of distributions to move toward a better mechanistic understanding. A physiologically based pharmacokinetic model presented here incorporates multiple means of drug distribution to assemble a model for understanding potential factors that may determine the distribution of drugs across various regions of the brain, including both intra- and extracellular regions. Two classes of parameters are presented. The first concerns regional gross anatomic variability of the brain; the second concerns estimation of unbound fractions of drugs using know membrane phospholipid heterogeneity derived from regional lipid content. The model was then tested by comparing its outcomes to data from published human pharmacokinetic studies of acetaminophen, morphine, and phenytoin. The alignment of model predictions in the plasma, CSF, and tissue concentrations with the published data from studies of those three drugs suggests that the model can be a template for identifying drug localization in the brain. Clearly, knowledge of differentiated drug distribution in the brain is a requisite step in postulating pharmacodynamic and certain disease mechanisms. SIGNIFICANCE STATEMENT: This study concerns the application of heterogenous lipid distribution in brain tissue to predict regional variations in drug distribution in the brain via a mathematical model, thus expanding upon the current understanding of mechanisms of drug distribution in the central nervous system.

PBPK-PD modeling for the preclinical development and clinical translation of tau antibodies for Alzheimer's disease

Disrupted tau proteostasis and transneuronal spread is a pathological hallmark of Alzheimer's disease. Neurodegenerative diseases remain an unmet medical need and novel disease modifying therapeutics are paramount. Our objective was to develop a mechanistic mathematical model to enhance our understanding of tau antibody pharmacokinetics and pharmacodynamics in animals and humans. A physiologically-based pharmacokinetic-pharmacodynamic (PBPK-PD) modeling approach was employed to support the preclinical development and clinical translation of therapeutic antibodies targeting tau for the treatment of Alzheimer's disease. The pharmacokinetics of a tau antibody was evaluated in rat and non-human primate microdialysis studies. Model validation for humans was performed using publicly available clinical data for gosuranemab. In-silico analyses were performed to predict tau engagement in human brain for a range of tau antibody affinities and various dosing regimens. PBPK-PD modeling enabled a quantitative understanding for the relationship between dose, affinity, and target engagement, which supported lead candidate optimization and predictions of clinically efficacious dosing regimens.

Revisiting Cerebrospinal Fluid Flow Direction and Rate in Physiologically Based Pharmacokinetic Model

The bidirectional pulsatile movement of cerebrospinal fluid (CSF), instead of the traditionally believed unidirectional and constant CSF circulation, has been demonstrated. In the present study, the structure and parameters of the CSF compartments were revisited in our comprehensive and validated central nervous system (CNS)-specific, physiologically based pharmacokinetic (PBPK) model of healthy rats (LeiCNS-PK3.0). The bidirectional and site-dependent CSF movement was incorporated into LeiCNS-PK3.0 to create the new LeiCNS-PK“3.1” model. The physiological CSF movement rates in healthy rats that are unavailable from the literature were estimated by fitting the PK data of sucrose, a CSF flow marker, after intra-CSF administration. The capability of LeiCNS-PK3.1 to describe the PK profiles of other molecules was compared with that of the original LeiCNS-PK3.0 model. LeiCNS-PK3.1 demonstrated superior description of the CSF PK profiles of a range of small molecules after intra-CSF administration over LeiCNS-PK3.0. LeiCNS-PK3.1 also retained the same level of predictability of CSF PK profiles in cisterna magna after intravenous administration. These results support the theory of bidirectional and site-dependent CSF movement across the entire CSF space over unidirectional and constant CSF circulation in healthy rats, pointing out the need to revisit the structures and parameters of CSF compartments in CNS-PBPK models.

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.

Drug Distribution in Brain and Cerebrospinal Fluids in Relation to IC50 Values in Aging and Alzheimer's Disease, Using the Physiologically Based LeiCNS-PK3.0 Model

Background

Very little knowledge exists on the impact of Alzheimer’s disease on the CNS target site pharmacokinetics (PK).

Aim

To predict the CNS PK of cognitively healthy young and elderly and of Alzheimer’s patients using the physiologically based LeiCNS-PK3.0 model.

Methods

LeiCNS-PK3.0 was used to predict the PK profiles in brain extracellular (brain_ECF) and intracellular (brain_ICF) fluids and cerebrospinal fluid of the subarachnoid space (CSF_SAS) of donepezil, galantamine, memantine, rivastigmine, and semagacestat in young, elderly, and Alzheimer’s patients. The physiological parameters of LeiCNS-PK3.0 were adapted for aging and Alzheimer’s based on an extensive literature search. The CNS PK profiles at plateau for clinical dose regimens were related to in vitro IC₅₀ values of acetylcholinesterase, butyrylcholinesterase, N-methyl-D-aspartate, or gamma-secretase.

Results

The PK profiles of all drugs differed between the CNS compartments regarding plateau levels and fluctuation. Brain_ECF, brain_ICF and CSF_SAS PK profile relationships were different between the drugs. Aging and Alzheimer’s had little to no impact on CNS PK. Rivastigmine acetylcholinesterase IC₅₀ values were not reached. Semagacestat brain PK plateau levels were below the IC₅₀ of gamma-secretase for half of the interdose interval, unlike CSF_SAS PK profiles that were consistently above IC_50.

Conclusion

This study provides insights into the relations between CNS compartments PK profiles, including target sites. CSF_SAS PK appears to be an unreliable predictor of brain PK. Also, despite extensive changes in blood-brain barrier and brain properties in Alzheimer’s, this study shows that the impact of aging and Alzheimer’s pathology on CNS distribution of the five drugs is insignificant.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-022-03281-3.

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.

The Extension of the LeiCNS-PK3.0 Model in Combination with the "Handshake" Approach to Understand Brain Tumor Pathophysiology

Micrometastatic brain tumor cells, which cause recurrence of malignant brain tumors, are often protected by the intact blood–brain barrier (BBB). Therefore, it is essential to deliver effective drugs across not only the disrupted blood-tumor barrier (BTB) but also the intact BBB to effectively treat malignant brain tumors. Our aim is to predict pharmacokinetic (PK) profiles in brain tumor regions with the disrupted BTB and the intact BBB to support the successful drug development for malignant brain tumors. LeiCNS-PK3.0, a comprehensive central nervous system (CNS) physiologically based pharmacokinetic (PBPK) model, was extended to incorporate brain tumor compartments. Most pathophysiological parameters of brain tumors were obtained from literature and two missing parameters of the BTB, paracellular pore size and expression level of active transporters, were estimated by fitting existing data, like a “handshake”. Simultaneous predictions were made for PK profiles in extracellular fluids (ECF) of brain tumors and normal-appearing brain and validated on existing data for six small molecule anticancer drugs. The LeiCNS-tumor model predicted ECF PK profiles in brain tumor as well as normal-appearing brain in rat brain tumor models and high-grade glioma patients within twofold error for most data points, in combination with estimated paracellular pore size of the BTB and active efflux clearance at the BTB. Our model demonstrated a potential to predict PK profiles of small molecule drugs in brain tumors, for which quantitative information on pathophysiological alterations is available, and contribute to the efficient and successful drug development for malignant brain tumors.

In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition

Drug development for the central nervous system (CNS) is a complex endeavour with low success rates, as the structural complexity of the brain and specifically the blood-brain barrier (BBB) poses tremendous challenges. Several in vitro brain systems have been evaluated, but the ultimate use of these data in terms of translation to human brain concentration profiles remains to be fully developed. Thus, linking up in vitro-to-in vivo extrapolation (IVIVE) strategies to physiologically based pharmacokinetic (PBPK) models of brain is a useful effort that allows better prediction of drug concentrations in CNS components. Such models may overcome some known aspects of inter-species differences in CNS drug disposition. Required physiological (i.e. systems) parameters in the model are derived from quantitative values in each organ. However, due to the inability to directly measure brain concentrations in humans, compound-specific (drug) parameters are often obtained from in silico or in vitro studies. Such data are translated through IVIVE which could be also applied to preclinical in vivo observations. In such exercises, the limitations of the assays and inter-species differences should be adequately understood in order to verify these predictions with the observed concentration data. This report summarizes the state of IVIVE-PBPK-linked models and discusses shortcomings and areas of further research for better prediction of CNS drug disposition.

Physiologically Based Modelling Framework for Prediction of Pulmonary Pharmacokinetics of Antimicrobial Target Site Concentrations

BACKGROUND AND OBJECTIVES

Prediction of antimicrobial target-site pharmacokinetics is of relevance to optimize treatment with antimicrobial agents. A physiologically based pharmacokinetic (PBPK) model framework was developed for prediction of pulmonary pharmacokinetics, including key pulmonary infection sites (i.e. the alveolar macrophages and the epithelial lining fluid).

METHODS

The modelling framework incorporated three lung PBPK models: a general passive permeability-limited model, a drug-specific permeability-limited model and a quantitative structure-property relationship (QSPR)-informed perfusion-limited model. We applied the modelling framework to three fluoroquinolone antibiotics. Incorporation of experimental drug-specific permeability data was found essential for accurate prediction.

RESULTS

In the absence of drug-specific transport data, our QSPR-based model has generic applicability. Furthermore, we evaluated the impact of drug properties and pathophysiologically related changes on pulmonary pharmacokinetics. Pulmonary pharmacokinetics were highly affected by physiological changes, causing a shift in the main route of diffusion (i.e. paracellular or transcellular). Finally, we show that lysosomal trapping can cause an overestimation of cytosolic concentrations for basic compounds when measuring drug concentrations in cell homogenate.

CONCLUSION

The developed lung PBPK model framework constitutes a promising tool for characterization of pulmonary exposure of systemically administrated antimicrobials.