Toward an improved prediction of humanin vivobrain penetration

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.

In Vitro Blood-Brain Barrier Models for Neuroinfectious Diseases: A Narrative Review

The blood-brain barrier (BBB) is a complex, dynamic, and adaptable barrier between the peripheral blood system and the central nervous system. While this barrier protects the brain and spinal cord from inflammation and infection, it prevents most drugs from reaching the brain tissue. With the expanding interest in the pathophysiology of BBB, the development of in vitro BBB models has dramatically evolved. However, due to the lack of a standard model, a range of experimental protocols, BBB-phenotype markers, and permeability flux markers was utilized to construct in vitro BBB models. Several neuroinfectious diseases are associated with BBB dysfunction. To conduct neuroinfectious disease research effectively, there stems a need to design representative in vitro human BBB models that mimic the BBB's functional and molecular properties. The highest necessity is for an in vitro standardised BBB model that accurately represents all the complexities of an intact brain barrier. Thus, this in-depth review aims to describe the optimization and validation parameters for building BBB models and to discuss previous research on neuroinfectious diseases that have utilized in vitro BBB models. The findings in this review may serve as a basis for more efficient optimisation, validation, and maintenance of a structurally- and functionally intact BBB model, particularly for future studies on neuroinfectious diseases.

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.

Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models

Machine learning (ML) approaches have been applied to predicting drug pharmacokinetic properties. Previously, we predicted rat unbound brain-to-plasma ratio (Kpuu,brain) by ML models. In this study, we aimed to predict human Kpuu,brain through animal ML models. First, we re-evaluated ML models for rat Kpuu,brain prediction by using trendy open-source packages. We then developed ML models for monkey Kpuu,brain prediction. Leave-one-out cross validation was utilized to rationally build models using a relatively small dataset. After establishing the monkey and rat ML models, human Kpuu,brain prediction was achieved by implementing the animal models considering appropriate scaling methods. Mechanistic NeuroPK models for the identical monkey and human dataset were treated as the criteria for comparison. Results showed that rat Kpuu,brain predictivity was successfully replicated. The optimal ML model for monkey Kpuu,brain prediction was superior to the NeuroPK model, where accuracy within 2-fold error was 78% (R2 = 0.76). For human Kpuu,brain prediction, rat model using relative expression factor (REF), scaled transporter efflux ratios (ERs), and monkey model using in vitro ERs can provide comparable predictivity to the NeuroPK model, where accuracy within 2-fold error was 71% and 64% (R2 = 0.30 and 0.52), respectively. We demonstrated that ML models can deliver promising Kpuu,brain prediction with several advantages: (1) predict reasonable animal Kpuu,brain; (2) prospectively predict human Kpuu,brain from animal models; and (3) can skip expensive monkey studies for human prediction by using the rat model. As a result, ML models can be a powerful tool for drug Kpuu,brain prediction in the discovery stage.

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

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

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.

Bazedoxifene - a promising brain active SERM that crosses the blood brain barrier and enhances spatial memory

Over 20 years of accumulated evidence has shown that the major female sex hormone 17β-estradiol can enhance cognitive functioning. However, the utility of estradiol as a therapeutic cognitive enhancer is hindered by its unwanted peripheral effects (carcinogenic). Selective estrogen receptor modulators (SERMs) avoid the unwanted effects of estradiol by acting as estrogen receptor antagonists in some tissues such as breast and uterus, but as agonists in others such as bone, and are currently used for the treatment of osteoporosis. However, understanding of their actions in the brain are limited. The third generation SERM bazedoxifene has recently been FDA approved for clinical use with an improved biosafety profile. However, whether bazedoxifene can enter the brain and enhance cognition is unknown. Using mice, the current study aimed to explore if bazedoxifene can 1) cross the blood-brain barrier, 2) rescue ovariectomy-induced hippocampal-dependent spatial memory deficit, and 3) activate neural estrogen response element (ERE)-dependent gene transcription. Using liquid chromatography-mass spectrometry (LC-MS), we firstly demonstrate that a peripheral injection of bazedoxifene can enter the brain. Secondly, we show that an acute intraperitoneal injection of bazedoxifene can rescue ovariectomy-induced spatial memory deficits. And finally, using the ERE-luciferase reporter mouse, we show in vivo that bazedoxifene can activate the ERE in the brain. The evidence shown here suggest bazedoxifene could be a viable cognitive enhancer with promising clinical applicability.

Reprint of: Nonclinical species sensitivity to convulsions: An IQ DruSafe consortium working group initiative

Clinical development of compounds that carry a convulsion liability is typically limited by safety margins based on the most sensitive nonclinical species. To better understand differences in sensitivity to drug-induced convulsion of commonly used preclinical species, a survey was distributed amongst pharmaceutical companies through an IQ consortium (International Consortium for Innovation and Quality in Pharmaceutical Development) resulting in convulsion-related data on 80 unique compounds from 11 companies. The lowest free drug plasma concentration at which convulsions were observed and the no observed effect level for convulsions were compared between species to determine their relative sensitivity. Additionally, data were collected on other endpoints including use of electroencephalography, premonitory signs, convulsion type, the reason why development was stopped, and the highest development phase reached. The key outcomes were: (1) the dog was most often determined to be the most sensitive species by both non-exposure and exposure-based analyses, (2) there was not a clear sensitivity ranking of other species (NHP, rat and mouse), (3) CNS symptoms were frequently present at exposures that were not associated with convulsions, but no single reliable premonitory indicator of convulsion was identified, and (4) the lack of convulsions in the compounds that were tested in humans in this dataset may suggest that convulsion liability is well mitigated via current drug development strategies.

A 3D brain unit model to further improve prediction of local drug distribution within the brain

The development of drugs targeting the brain still faces a high failure rate. One of the reasons is a lack of quantitative understanding of the complex processes that govern the pharmacokinetics (PK) of a drug within the brain. While a number of models on drug distribution into and within the brain is available, none of these addresses the combination of factors that affect local drug concentrations in brain extracellular fluid (brain ECF). Here, we develop a 3D brain unit model, which builds on our previous proof-of-concept 2D brain unit model, to understand the factors that govern local unbound and bound drug PK within the brain. The 3D brain unit is a cube, in which the brain capillaries surround the brain ECF. Drug concentration-time profiles are described in both a blood-plasma-domain and a brain-ECF-domain by a set of differential equations. The model includes descriptions of blood plasma PK, transport through the blood-brain barrier (BBB), by passive transport via paracellular and transcellular routes, and by active transport, and drug binding kinetics. The impact of all these factors on ultimate local brain ECF unbound and bound drug concentrations is assessed. In this article we show that all the above mentioned factors affect brain ECF PK in an interdependent manner. This indicates that for a quantitative understanding of local drug concentrations within the brain ECF, interdependencies of all transport and binding processes should be understood. To that end, the 3D brain unit model is an excellent tool, and can be used to build a larger network of 3D brain units, in which the properties for each unit can be defined independently to reflect local differences in characteristics of the brain.

Impact of In Vitro Passive Permeability in a P-gp-transfected LLC-PK1 Model on the Prediction of the Rat and Human Unbound Brain-to-Plasma Concentration Ratio

PURPOSE

More accurate prediction of the extent of drug brain exposure in early drug discovery and understanding potential species differences could help to guide medicinal chemistry and avoid unnecessary animal studies. Hence, the aim of the current study was to validate the use of a P-gp transfected LLC-PK1 model to predict the unbound brain-to-plasma concentration ratio (Kp_uu,brain) in rats and humans.

METHODS

MOCK-, Mdr1a- and MDR1-transfected LLC-PK1 monolayers were applied in a transwell setup to quantify the bidirectional transport for 12 specific P-gp substrates, 48 UCB drug discovery compounds, 11 compounds with reported rat in situ brain perfusion data and 6 compounds with reported human Kp_uu,brain values. The in vitro transport data were introduced in a minimal PBPK model (SIVA®) to determine the transport parameters. These parameters were combined with the differences between in vitro and in vivo passive permeability as well as P-gp expression levels (as determined by LC-MS/MS), to predict the Kp_uu,brain.

RESULTS

A 10-fold difference between in vitro and in vivo passive permeability was observed. Incorporation of the differences between in vitro and in vivo passive permeability and P-gp expression levels resulted in an improved prediction of rat (AAFE 2.17) and human Kp_uu,brain (AAFE 2.10).

CONCLUSIONS

We have succesfully validated a methodology to use a P-gp overexpressing LLC-PK1 cell line to predict both rat and human Kp_uu,brain by correcting for both passive permeability and P-gp expression levels.

Brain Distribution of Drugs: Pharmacokinetic Considerations

It is crucial to understand the basic principles of drug transport, from the site of delivery to the site of action within the CNS, in order to evaluate the possible utility of a new drug candidate for CNS action, or possible CNS side effects of non-CNS targeting drugs. This includes pharmacokinetic aspects of drug concentration-time profiles in plasma and brain, blood-brain barrier transport and drug distribution within the brain parenchyma as well as elimination processes from the brain. Knowledge of anatomical and physiological aspects connected with drug delivery is crucial in this context. The chapter is intended for professionals working in the field of CNS drug development and summarizes key pharmacokinetic principles and state-of-the-art experimental methodologies to assess brain drug disposition. Key parameters, describing the extent of unbound (free) drug across brain barriers, in particular blood-brain and blood-cerebrospinal fluid barriers, are presented along with their application in drug development. Special emphasis is given to brain intracellular pharmacokinetics and its role in evaluating target engagement. Fundamental neuropharmacokinetic differences between small molecular drugs and biologicals are discussed and critical knowledge gaps are outlined.

A Comparative Study of in vitro Assays for Predicting the Nonspecific Binding of PET Imaging Agents in vivo

Nonspecific binding (NSB) is a key parameter in optimizing PET imaging tracers. We compared the ability to predict NSB of three available methods: LIMBA, rat f_u,brain , and CHI(IAM). Even though NSB is often associated with lipophilicity, we observed that logD does not correlate with any of these assays, clearly indicating that lipophilicity, while influencing NSB, is insufficient to predict it. A cross-comparison of the methods showed that all three correlate and are useful predictors of NSB. The three assays, however, rank the molecules slightly differently, illustrating the challenge of comparing molecules within a narrow chemical space. We also noted that CHI(IAM) values more effectively predict V_NS , a measure of in vivo NSB in the human brain. CHI(IAM) measurements might be a closer model of the actual physicochemical interaction between PET tracer candidates and cell membranes, and seems to be the method of choice for the optimization of in vivo NSB.

Investigating the Impact of Albumin on the Liver Uptake of Pitavastatin and Warfarin in Nagase Analbuminemic Rats

Albumin has been suggested to enhance the hepatic uptake of organic anion-transporting polypeptide (Oatp) substrates in various in vitro as well as liver perfusion models. However, it is not known whether the interplay between albumin and Oatp substrates is an experimental artifact or if this interaction occurs in vivo. The objective of this work was to investigate the hepatic uptake of warfarin and pitavastatin, which are both extensively bound to albumin but only pitavastatin being an Oatp substrate. Experiments were conducted in Nagase analbuminemic rats (NAR) which exhibit reduced albumin levels compared with F344 (wild type, WT). The fraction unbound (f _u) was 140- and 10-fold greater in NAR plasma for warfarin and pitavastatin, respectively, whereas no meaningful differences were observed with tissue binding. In vitro, pitavastatin uptake into hepatocytes reconstituted in WT plasma was 17- and 3-fold greater than when reconstituted in buffer or NAR plasma, respectively. In vivo, the free tissue-to-free plasma ratios (K _p,u,u) from brain and liver in intact WT and NAR were not significantly different for warfarin. Contrarily, liver K _p,u,u of pitavastatin was 6-fold higher in WT animals, which corresponded to a 2.3-fold reduction in free plasma and 2.6-fold increase in free liver exposure. These results suggest that the enhanced hepatic uptake by albumin is not necessarily an experimental artifact but is also a relevant phenomenon in vivo. This work raises the possibility that other plasma proteins may also effect the function of additional drug transporters, and that modulating plasma protein binding may exhibit meaningful clinical relevance in the disposition of drugs. SIGNIFICANCE STATEMENT: The interplay between albumin and Oatp substrates has been reported in hepatocytes and in liver perfusion studies, but the in vivo relevance of this interaction has yet to be elucidated. Using NAR and its corresponding WT animal, this study demonstrates that albumin may indeed enhance the hepatic uptake of pitavastatin in intact animals. In vivo demonstration of this interplay not only provides further justification for continued investigation into this particular mechanism but also raises the possibility that other plasma proteins may affect additional drug transporters and that modulating plasma protein binding may exhibit meaningful clinical relevance in the disposition of drugs.

Heterogeneous drug tissue binding in brain regions of rats, Alzheimer's patients and controls: impact on translational drug development

For preclinical and clinical assessment of therapeutically relevant unbound, free, brain concentrations, the pharmacokinetic parameter fraction of unbound drug in brain (f_u,brain) is commonly used to compensate total drug concentrations for nonspecific brain tissue binding (BTB). As, homogenous BTB is assumed between species and in health and disease, rat BTB is routinely used. The impact of Alzheimer’s disease (AD) on drug BTB in brain regions of interest (ROI), i.e., f_u,brain,ROI, is yet unclear. This study for the first time provides insight into regional drug BTB and the validity of employing rat f_u,brain,ROI as a surrogate of human BTB, by investigating five marketed drugs in post-mortem tissue from AD patients (n = 6) and age-matched controls (n = 6). Heterogeneous drug BTB was observed in all within group comparisons independent of disease and species. The findings oppose the assumption of uniform BTB, highlighting the need of case-by-case evaluation of f_u,brain,ROI in translational CNS research.

Prediction of brain:blood unbound concentration ratios in CNS drug discovery employing in silico and in vitro model systems

Recent years have seen a paradigm shift away from optimizing the brain:blood concentration ratio toward the more relevant brain:blood unbound concentration ratio (K_p,uu,br) in CNS drug discovery. Here, we review the recent developments in the in silico and in vitro model systems to predict the K_p,uu,br of discovery compounds with special emphasis on the in-vitro-in-vivo correlation. We also discuss clinical 'translation' of rodent K_p,uu,br and highlight the future directions for improvement in brain penetration prediction. Important in this regard are in silico K_p,uu,br models built on larger datasets of high quality, calibration and deeper understanding of experimental in vitro transporter systems, and better understanding of blood-brain barrier transporters and their in vivo relevance aside from P-gp and BCRP.

Comparison of Species and Cell-Type Differences in Fraction Unbound of Liver Tissues, Hepatocytes, and Cell Lines

Fraction unbound (f_u) of liver tissue, hepatocytes, and other cell types is an essential parameter used to estimate unbound liver drug concentration and intracellular free drug concentration. f_u,liver and f_u,cell are frequently measured in multiple species and cell types in drug discovery and development for various applications. A comparison study of 12 matrices for f_u,liver and f_u,cell of hepatocytes in five different species (mouse, rat, dog, monkey, and human), as well as f_u,cell of Huh7 and human embryonic kidney 293 cell lines, was conducted for 22 structurally diverse compounds with the equilibrium dialysis method. Using an average bioequivalence approach, our results show that the average difference in binding to liver tissue, hepatocytes, or different cell types was within 2-fold of that of the rat f_u,liver Therefore, we recommend using rat f_u,liver as a surrogate for liver binding in other species and cell types in drug discovery. This strategy offers the potential to simplify binding studies and reduce cost, thereby enabling a more effective and practical determination of f_u for liver tissues, hepatocytes, and other cell types. In addition, f_u under hepatocyte stability incubation conditions should not be confused with f_u,cell, as one is a diluted f_u and the other is an undiluted f_u Cell density also plays a critical role in the accurate measurement of f_u,cell.

Development of a Region-Specific Physiologically Based Pharmacokinetic Brain Model to Assess Hippocampus and Frontal Cortex Pharmacokinetics

Central nervous system drug discovery and development is hindered by the impermeable nature of the blood-brain barrier. Pharmacokinetic modeling can provide a novel approach to estimate CNS drug exposure; however, existing models do not predict temporal drug concentrations in distinct brain regions. A rat CNS physiologically based pharmacokinetic (PBPK) model was developed, incorporating brain compartments for the frontal cortex (FC), hippocampus (HC), "rest-of-brain" (ROB), and cerebrospinal fluid (CSF). Model predictions of FC and HC C_max, t_max and AUC were within 2-fold of that reported for carbamazepine and phenytoin. The inclusion of a 30% coefficient of variation on regional brain tissue volumes, to assess the uncertainty of regional brain compartments volumes on predicted concentrations, resulted in a minimal level of sensitivity of model predictions. This model was subsequently extended to predict human brain morphine concentrations, and predicted a ROB C_max of 21.7 ± 6.41 ng/mL when compared to "better" (10.1 ng/mL) or "worse" (29.8 ng/mL) brain tissue regions with a FC C_max of 62.12 ± 17.32 ng/mL and a HC C_max of 182.2 ± 51.2 ng/mL. These results indicate that this simplified regional brain PBPK model is useful for forward prediction approaches in humans for estimating regional brain drug concentrations.

Development of a population pharmacokinetic model to predict brain distribution and dopamine D2 receptor occupancy of raclopride in non-anesthetized rat

BACKGROUND

Raclopride is a selective antagonist of the dopamine D2 receptor. It is one of the most frequently used in vivo D2 tracers (at low doses) for assessing drug-induced receptor occupancy (RO) in animals and humans. It is also commonly used as a pharmacological blocker (at high doses) to occupy the available D2 receptors and antagonize the action of dopamine or drugs on D2 in preclinical studies. The aims of this study were to comprehensively evaluate its pharmacokinetic (PK) profiles in different brain compartments and to establish a PK-RO model that could predict the brain distribution and RO of raclopride in the freely moving rat using a LC-MS based approach.

METHODS

Rats (n=24) received a 10-min IV infusion of non-radiolabeled raclopride (1.61μmol/kg, i.e. 0.56mg/kg). Plasma and the brain tissues of striatum (with high density of D2 receptors) and cerebellum (with negligible amount of D2 receptors) were collected. Additional microdialysis experiments were performed in some rats (n=7) to measure the free drug concentration in the extracellular fluid of the striatum and cerebellum. Raclopride concentrations in all samples were analyzed by LC-MS. A population PK-RO model was constructed in NONMEM to describe the concentration-time profiles in the unbound plasma, brain extracellular fluid and brain tissue compartments and to estimate the RO based on raclopride-D2 receptor binding kinetics.

RESULTS

In plasma raclopride showed a rapid distribution phase followed by a slower elimination phase. The striatum tissue concentrations were consistently higher than that of cerebellum tissue throughout the whole experimental period (10-h) due to higher non-specific tissue binding and D2 receptor binding in the striatum. Model-based simulations accurately predicted the literature data on rat plasma PK, brain tissue PK and D2 RO at different time points after intravenous or subcutaneous administration of raclopride at tracer dose (RO <10%), sub-pharmacological dose (RO 10%-30%) and pharmacological dose (RO >30%).

CONCLUSION

For the first time a predictive model that could describe the quantitative in vivo relationship between dose, PK and D2 RO of raclopride in non-anesthetized rat was established. The PK-RO model could facilitate the selection of optimal dose and dosing time when raclopride is used as tracer or as pharmacological blocker in various rat studies. The LC-MS based approach, which doses and quantifies a non-radiolabeled tracer, could be useful in evaluating the systemic disposition and brain kinetics of tracers.

Elucidating the Aβ42 Anti-Aggregation Mechanism of Action of Tramiprosate in Alzheimer's Disease: Integrating Molecular Analytical Methods, Pharmacokinetic and Clinical Data

BACKGROUND

Amyloid beta (Aβ) oligomers play a critical role in the pathogenesis of Alzheimer's disease (AD) and represent a promising target for drug development. Tramiprosate is a small-molecule Aβ anti-aggregation agent that was evaluated in phase III clinical trials for AD but did not meet the primary efficacy endpoints; however, a pre-specified subgroup analysis revealed robust, sustained, and clinically meaningful cognitive and functional effects in patients with AD homozygous for the ε4 allele of apolipoprotein E4 (APOE4/4 homozygotes), who carry an increased risk for the disease. Therefore, to build on this important efficacy attribute and to further improve its pharmaceutical properties, we have developed a prodrug of tramiprosate ALZ-801 that is in advanced stages of clinical development. To elucidate how tramiprosate works, we investigated its molecular mechanism of action (MOA) and the translation to observed clinical outcomes.

OBJECTIVE

The two main objectives of this research were to (1) elucidate and characterize the MOA of tramiprosate via an integrated application of three independent molecular methodologies and (2) present an integrated translational analysis that links the MOA, conformation of the target, stoichiometry, and pharmacokinetic dose exposure to the observed clinical outcome in APOE4/4 homozygote subjects.

METHOD

We used three molecular analytical methods-ion mobility spectrometry-mass spectrometry (IMS-MS), nuclear magnetic resonance (NMR), and molecular dynamics-to characterize the concentration-related interactions of tramiprosate versus Aβ42 monomers and the resultant conformational alterations affecting aggregation into oligomers. The molecular stoichiometry of the tramiprosate versus Aβ42 interaction was further analyzed in the context of clinical pharmacokinetic dose exposure and central nervous system Aβ42 levels (i.e., pharmacokinetic-pharmacodynamic translation in humans).

RESULTS

We observed a multi-ligand interaction of tramiprosate with monomeric Aβ42, which differs from the traditional 1:1 binding. This resulted in the stabilization of Aβ42 monomers and inhibition of oligomer formation and elongation, as demonstrated by IMS-MS and molecular dynamics. Using NMR spectroscopy and molecular dynamics, we also showed that tramiprosate bound to Lys16, Lys28, and Asp23, the key amino acid side chains of Aβ42 that are responsible for both conformational seed formation and neuronal toxicity. The projected molar excess of tramiprosate versus Aβ42 in humans using the dose effective in patients with AD aligned with the molecular stoichiometry of the interaction, providing a clear clinical translation of the MOA. A consistent alignment of these preclinical-to-clinical elements describes a unique example of translational medicine and supports the efficacy seen in symptomatic patients with AD. This unique "enveloping mechanism" of tramiprosate also provides a potential basis for tramiprosate dose selection for patients with homozygous AD at earlier stages of disease.

CONCLUSION

We have identified the molecular mechanism that may account for the observed clinical efficacy of tramiprosate in patients with APOE4/4 homozygous AD. In addition, the integrated application of the molecular methodologies (i.e., IMS-MS, NMR, and thermodynamics analysis) indicates that it is feasible to modulate and control the Aβ42 conformational dynamics landscape by a small molecule, resulting in a favorable Aβ42 conformational change that leads to a clinically relevant amyloid anti-aggregation effect and inhibition of oligomer formation. This novel enveloping MOA of tramiprosate has potential utility in the development of disease-modifying therapies for AD and other neurodegenerative diseases caused by misfolded proteins.

Imaging in Central Nervous System Drug Discovery

The discovery and development of central nervous system (CNS) drugs is an extremely challenging process requiring large resources, timelines, and associated costs. The high risk of failure leads to high levels of risk. Over the past couple of decades PET imaging has become a central component of the CNS drug-development process, enabling decision-making in phase I studies, where early discharge of risk provides increased confidence to progress a candidate to more costly later phase testing at the right dose level or alternatively to kill a compound through failure to meet key criteria. The so called "3 pillars" of drug survival, namely; tissue exposure, target engagement, and pharmacologic activity, are particularly well suited for evaluation by PET imaging. This review introduces the process of CNS drug development before considering how PET imaging of the "3 pillars" has advanced to provide valuable tools for decision-making on the critical path of CNS drug development. Finally, we review the advances in PET science of biomarker development and analysis that enable sophisticated drug-development studies in man.

QSAR Model of Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain: Incorporating P-glycoprotein Efflux as a Variable

We report development and prospective validation of a QSAR model of the unbound brain-to-plasma partition coefficient, K_p,uu,brain, based on the in-house data set of ∼1000 compounds. We discuss effects of experimental variability, explore the applicability of both regression and classification approaches, and evaluate a novel, model-within-a-model approach of including P-glycoprotein efflux prediction as an additional variable. When tested on an independent test set of 91 internal compounds, incorporation of P-glycoprotein efflux information significantly improves the model performance resulting in an R² of 0.53, RMSE of 0.57, Spearman's Rho correlation coefficient of 0.73, and qualitative prediction accuracy of 0.8 (kappa = 0.6). In addition to improving the performance, one of the key advantages of this approach is the larger chemical space coverage provided indirectly through incorporation of the in vitro, higher throughput data set that is 4 times larger than the in vivo data set.

Considerations in the Development of Reversibly Binding PET Radioligands for Brain Imaging

The development of reversibly binding radioligands for imaging brain proteins in vivo, such as enzymes, neurotransmitter transporters, receptors and ion channels, with positron emission tomography (PET) is keenly sought for biomedical studies of neuropsychiatric disorders and for drug discovery and development, but is recognized as being highly challenging at the medicinal chemistry level. This article aims to compile and discuss the main considerations to be taken into account by chemists embarking on programs of radioligand development for PET imaging of brain protein targets.

Comparing translational population-PBPK modelling of brain microdialysis with bottom-up prediction of brain-to-plasma distribution in rat and human

The prediction of brain extracellular fluid (ECF) concentrations in human is a potentially valuable asset during drug development as it can provide the pharmacokinetic input for pharmacokinetic-pharmacodynamic models. This study aimed to compare two translational modelling approaches that can be applied at the preclinical stage of development in order to simulate human brain ECF concentrations. A population-PBPK model of the central nervous system was developed based on brain microdialysis data, and the model parameters were translated to their corresponding human values to simulate ECF and brain tissue concentration profiles. In parallel, the PBPK modelling software Simcyp was used to simulate human brain tissue concentrations, via the bottom-up prediction of brain tissue distribution using two different sets of mechanistic tissue composition-based equations. The population-PBPK and bottom-up approaches gave similar predictions of total brain concentrations in both rat and human, while only the population-PBPK model was capable of accurately simulating the rat ECF concentrations. The choice of PBPK model must therefore depend on the purpose of the modelling exercise, the in vitro and in vivo data available and knowledge of the mechanisms governing the membrane permeability and distribution of the drug.

Permeability analysis of neuroactive drugs through a dynamic microfluidic in vitro blood-brain barrier model

This paper presents the permeability analysis of neuroactive drugs and correlation with in vivo brain/plasma ratios in a dynamic microfluidic blood-brain barrier (BBB) model. Permeability of seven neuroactive drugs (Ethosuximide, Gabapentin, Sertraline, Sunitinib, Traxoprodil, Varenicline, PF-304014) and trans-endothelial electrical resistance (TEER) were quantified in both dynamic (microfluidic) and static (transwell) BBB models, either with brain endothelial cells (bEnd.3) in monoculture, or in co-culture with glial cells (C6). Dynamic cultures were exposed to 15 dyn/cm(2) shear stress to mimic the in vivo environment. Dynamic models resulted in significantly higher average TEER (respective 5.9-fold and 8.9-fold increase for co-culture and monoculture models) and lower drug permeabilities (average respective decrease of 0.050 and 0.052 log(cm/s) for co-culture and monoculture) than static models; and co-culture models demonstrated higher average TEER (respective 90 and 25% increase for static and dynamic models) and lower drug permeability (average respective decrease of 0.063 and 0.061 log(cm/s) for static and dynamic models) than monoculture models. Correlation of the resultant logP e values [ranging from -4.06 to -3.63 log(cm/s)] with in vivo brain/plasma ratios (ranging from 0.42 to 26.8) showed highly linear correlation (R (2) > 0.85) for all model conditions, indicating the feasibility of the dynamic microfluidic BBB model for prediction of BBB clearance of pharmaceuticals.

Challenges of using in vitro data for modeling P-glycoprotein efflux in the blood-brain barrier

The efficacy of central nervous system (CNS) drugs may be limited by their poor ability to cross the bloodbrain barrier (BBB). Transporters, such as p-glycoprotein, may affect the distribution of many drugs into the CNS in conjunction with the restricted paracellular pathway of the BBB. It is therefore important to gain information on unbound drug concentrations in the brain in drug development to ensure sufficient drug exposure from plasma at the target site in the CNS. In vitro methods are routinely used in drug development to study passive permeability and p-glycoprotein efflux of new drugs. This review discusses the challenges in the use of in vitro data as input parameters in physiologically based pharmacokinetic (PBPK) models of CNS drug disposition of p-glycoprotein substrates. Experience with quinidine demonstrates the variability in in vitro parameters of passive permeability and active pglycoprotein efflux. Further work is needed to generate parameter values that are independent of the model and assay. This is a prerequisite for reliable predictions of drug concentrations in the brain in vivo.

Rational use of plasma protein and tissue binding data in drug design

It is a commonly accepted assumption that only unbound drug molecules are available to interact with their targets. Therefore, one of the objectives in drug design is to optimize the compound structure to increase in vivo unbound drug concentration. In this review, theoretical analyses and experimental observations are presented to illustrate that low plasma protein binding does not necessarily lead to high in vivo unbound plasma concentration. Similarly, low brain tissue binding does not lead to high in vivo unbound brain tissue concentration. Instead, low intrinsic clearance leads to high in vivo unbound plasma concentration, and low efflux transport activity at the blood-brain barrier leads to high unbound brain concentration. Plasma protein and brain tissue binding are very important parameters in understanding pharmacokinetics, pharmacodynamics, and toxicities of drugs, but these parameters should not be targeted for optimization in drug design.

Single compartment drug delivery

Drug design is built on the concept that key molecular targets of disease are isolated in the diseased tissue. Systemic drug administration would be sufficient for targeting in such a case. It is, however, common for enzymes or receptors that are integral to disease to be structurally similar or identical to those that play important biological roles in normal tissues of the body. Additionally, systemic administration may not lead to local drug concentrations high enough to yield disease modification because of rapid systemic metabolism or lack of sufficient partitioning into the diseased tissue compartment. This review focuses on drug delivery methods that physically target drugs to individual compartments of the body. Compartments such as the bladder, peritoneum, brain, eye and skin are often sites of disease and can sometimes be viewed as "privileged," since they intrinsically hinder partitioning of systemically administered agents. These compartments have become the focus of a wide array of procedures and devices for direct administration of drugs. We discuss the rationale behind single compartment drug delivery for each of these compartments, and give an overview of examples at different development stages, from the lab bench to phase III clinical trials to clinical practice. We approach single compartment drug delivery from both a translational and a technological perspective.

Physiologically based pharmacokinetic modelling of drug penetration across the blood-brain barrier--towards a mechanistic IVIVE-based approach

Predicting the penetration of drugs across the human blood-brain barrier (BBB) is a significant challenge during their development. A variety of in vitro systems representing the BBB have been described, but the optimal use of these data in terms of extrapolation to human unbound brain concentration profiles remains to be fully exploited. Physiologically based pharmacokinetic (PBPK) modelling of drug disposition in the central nervous system (CNS) currently consists of fitting preclinical in vivo data to compartmental models in order to estimate the permeability and efflux of drugs across the BBB. The increasingly popular approach of using in vitro-in vivo extrapolation (IVIVE) to generate PBPK model input parameters could provide a more mechanistic basis for the interspecies translation of preclinical models of the CNS. However, a major hurdle exists in verifying these predictions with observed data, since human brain concentrations can't be directly measured. Therefore a combination of IVIVE-based and empirical modelling approaches based on preclinical data are currently required. In this review, we summarise the existing PBPK models of the CNS in the literature, and we evaluate the current opportunities and limitations of potential IVIVE strategies for PBPK modelling of BBB penetration.

Modeling of PET data in CNS drug discovery and development

Positron emission tomography (PET) is increasingly used in drug discovery and development for evaluation of CNS drug disposition and for studies of disease biomarkers to monitor drug effects on brain pathology. The quantitative analysis of PET data is based on kinetic modeling of radioactivity concentrations in plasma and brain tissue compartments. A number of quantitative methods of analysis have been developed that allow the determination of parameters describing drug pharmacokinetics and interaction with target binding sites in the brain. The optimal method of quantification depends on the properties of the radiolabeled drug or radioligand and the binding site studied. We here review the most frequently used methods for quantification of PET data in relation to CNS drug discovery and development. The utility of PET kinetic modeling in the development of novel CNS drugs is illustrated by examples from studies of the brain kinetic properties of radiolabeled drug molecules.

Advances in PET imaging of P-glycoprotein function at the blood-brain barrier

Efflux transporter P-glycoprotein (P-gp) at the blood-brain barrier (BBB) restricts substrate compounds from entering the brain and may thus contribute to pharmacoresistance observed in patient groups with refractory epilepsy and HIV. Altered P-gp function has also been implicated in neurodegenerative diseases such as Alzheimer's and Parkinson's disease. Positron emission tomography (PET), a molecular imaging modality, has become a promising method to study the role of P-gp at the BBB. The first PET study of P-gp function was conducted in 1998, and during the past 15 years two main categories of P-gp PET tracers have been investigated: tracers that are substrates of P-gp efflux and tracers that are inhibitors of P-gp function. PET, as a noninvasive imaging technique, allows translational research. Examples of this are preclinical investigations of P-gp function before and after administering P-gp modulating drugs, investigations in various animal and disease models, and clinical investigations regarding disease and aging. The objective of the present review is to give an overview of available PET radiotracers for studies of P-gp and to discuss how such studies can be designed. Further, the review summarizes results from PET studies of P-gp function in different central nervous system disorders.

Association between striatal subregions and extrastriatal regions in dopamine D(1) receptor expression: a positron emission tomography study

The mesencephalic dopamine (DA) system is the main DA system related to affective and cognitive functions. The system consists of two different cell groups, A9 and A10, which originate from different regions of the midbrain. The striatum is the main input from the midbrain, and is functionally organized into associative, sensorimotor and limbic subdivisions. At present, there have been few studies investigating the associations of DA functions between striatal subdivisions and extrastriatal regions. The aim of this study was to investigate the relationship of DA D₁ receptor (D₁R) expression between striatal subdivisions and extrastriatal regions in humans using positron emission tomography (PET) with voxel-by-voxel whole brain analysis. The PET study was performed on 30 healthy subjects using [¹¹C]SCH23390 to measure D₁R expression. Parametric images of binding potentials (BP_ND) were created using the simplified reference tissue model. Regions of interest were defined for striatal subdivisions. Multiple regression analysis was undertaken to determine extrastriatal regions that were associated with each striatal subdivision in BP_ND using statistical parametric mapping 5. The BP_ND values of associative, sensorimotor and limbic subdivisions were similarly correlated with those of multiple brain regions. Regarding the interrelationships among striatal subdivisions, mutual correlations were found among associative, sensorimotor and limbic subdivisions in BP_ND as well. The relationships in BP_ND between striatal subdivisions and extra-striatal regions suggest that differential striatal subdivisions and extrastriatal regions have a similar biological basis of D₁R expression. Different DA projections from the midbrain did not explain the associations between striatal subdivisions and extrastriatal regions in D₁R expression, and the DA-related neural networks among the midbrain, striatum and the other regions would contribute to a similar D₁R expression pattern throughout the whole brain.