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

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

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

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

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

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

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

Naltrexone Transport by a Proton-Coupled Organic Cation Antiporter in hCMEC/D3 Cells, an in Vitro Human Blood-Brain Barrier Model

Naltrexone is a mu-opioid receptor antagonist used in the treatment of opioid and alcohol dependence. The blood-brain barrier (BBB) transport characteristics of naltrexone was investigated by means of hCMEC/D3 cells, a human immortalized brain capillary endothelial cell line. In hCMEC/D3 cells, naltrexone is taken up in a concentration-dependent manner. Furthermore, naltrexone uptake significantly decreased in the presence of H⁺/organic cation (OC) antiporter substrates, during the little alteration exhibited by substrates of well-identified OC transporters classified into SLC22A family. Although naltrexone uptake by hCMEC/D3 cells was partially affected by changes of ionic conditions, it was markedly decreased in the presence of the metabolic inhibitor sodium azide. Furthermore, when treated by ammonium chloride, naltrexone uptake by hCMEC/D3 cells was altered by intracellular acidification and alkalization, suggesting the involvement of oppositely directed proton gradient in naltrexone transport across the BBB. The results obtained in the present in vitro study suggest the active transport of naltrexone from blood to the brain across the BBB by the H⁺/OC antiporter.

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.

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.

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.

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

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

The Brain Exposure Efficiency (BEE) Score

The blood-brain barrier (BBB), composed of microvascular tight junctions and glial cell sheathing, selectively controls drug permeation into the central nervous system (CNS) by either passive diffusion or active transport. Computational techniques capable of predicting molecular brain penetration are important to neurological drug design. A novel prediction algorithm, termed the Brain Exposure Efficiency Score (BEE), is presented. BEE addresses the need to incorporate the role of trans-BBB influx and efflux active transporters by considering key brain penetrance parameters, namely, steady state unbound brain to plasma ratio of drug (K_p,uu) and dose normalized unbound concentration of drug in brain (C_u,b). BEE was devised using quantitative structure-activity relationships (QSARs) and molecular modeling studies on known transporter proteins and their ligands. The developed algorithms are provided as a user-friendly open source calculator to assist in optimizing a brain penetrance strategy during the early phases of small molecule molecular therapeutic design.

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.

Investigation of MDR1-overexpressing cell lines to derive a quantitative prediction approach for brain disposition using in vitro efflux activities

MDR1-overexpressing Lilly Laboratories cell porcine kidney 1 cells (LLC-PK1-MDR1) and Madin-Darby canine kidney cells (MDCK-MDR1) are widely used in drug discovery to evaluate the in vivo relevance of MDR1-mediated efflux. However, as the in vitro efflux ratio (ER) of these cell lines are variable among research facilities, the in vitro ER of these cell lines that would affect quantitative predictivity of brain disposition has not been fully clarified. The aim of this study was to examine the effect of ER on the quantitative predictivity of brain disposition toward compounds with MDR1 and/or breast cancer resistant protein (BCRP) liabilities. Test compounds including internal molecules and five typical substrates of MDR1 and/or BCRP were assessed via an in vitro transporter assay to determine the corrected flux ratio (CFR) and an in vivo animal study using wild-type (WT) and Mdr1a (-/-)/Bcrp(-/-) (dual KO) rats. To assess the in vivo ER for MDR1, the two cell lines LLC-PK1-MDR1 and MDCK-MDR1 were used. After intravenously administering 29 test compounds to rats, the K_p,brain ratio (ratio of K_p,brain,WT to K_{p,brain,dual KO}), which is considered to be the unbound plasma-to-brain ratio (K_p,uu,brain) that does not require correction for protein binding in both plasma and brain, was determined by measuring their concentrations in the plasma and brain. The K_p,brain ratio of these compounds was predicted by fitting scaling factor that was extrapolated from the in vitro to in vivo ER for MDR1 and BCRP, defined as α and β, respectively. K_p,brain ratio values of 83% and 68% of compounds were predicted by using MDCK-MDR1 and LLC-PK1-MDR1, respectively, within a 2-fold range of the actual corresponding values. The α predicted from CFRs of MDCK-MDR1 was 47-fold smaller than that of LLC-PK1-MDR1; however, a dramatic change in β was not observed. This result appears to be consistent with the data of in vitro transport activity of MDR1, which was estimated to be ~28-fold higher in MDCK-MDR1 than in LLC-PK1-MDR1 by correlation analysis with CFR. Through this study, we revealed that 1) brain disposition in rats was well-predicted by considering the in vitro efflux activities for both MDR1 and BCRP, and 2) MDCK-MDR1 was the superior cell line for the quantitative prediction of brain disposition.

The Blood-Brain Barrier (BBB) Score

The blood-brain barrier (BBB) protects the brain from the toxic side effects of drugs and exogenous molecules. However, it is crucial that medications developed for neurological disorders cross into the brain in therapeutic concentrations. Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated "BBB Score", composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. On the basis of statistical analyses of our results, the BBB Score outperformed (AUC = 0.86) currently employed MPO approaches (MPO, AUC = 0.61; MPO_V2, AUC = 0.67). Initial evaluation of physicochemical property space using the BBB Score is a valuable addition to currently available drug design algorithms.

Isolation of Cerebral Capillaries from Fresh Human Brain Tissue

Understanding blood-brain barrier function under physiological and pathophysiological conditions is critical for the development of new therapeutic strategies that hold the promise to enhance brain drug delivery, improve brain protection, and treat brain disorders. However, studying the human blood-brain barrier function is challenging. Thus, there is a critical need for appropriate models. In this regard, brain capillaries isolated from human brain tissue represent a unique tool to study barrier function as close to the human in vivo situation as possible. Here, we describe an optimized protocol to isolate capillaries from human brain tissue at a high yield and with consistent quality and purity. Capillaries are isolated from fresh human brain tissue using mechanical homogenization, density-gradient centrifugation, and filtration. After the isolation, the human brain capillaries can be used for various applications including leakage assays, live cell imaging, and immune-based assays to study protein expression and function, enzyme activity, or intracellular signaling. Isolated human brain capillaries are a unique model to elucidate the regulation of the human blood-brain barrier function. This model can provide insights into central nervous system (CNS) pathogenesis, which will help the development of therapeutic strategies for treating CNS disorders.

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.

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.

Drug delivery to melanoma brain metastases: Can current challenges lead to new opportunities?

Melanoma has a high propensity to metastasize to the brain, and patients with melanoma brain metastases (MBM) have an extremely poor prognosis. The recent approval of several molecularly-targeted agents (e.g., BRAF, MEK inhibitors) and biologics (anti-CTLA-4, anti-PD-1 and anti-PD-L1 antibodies) has brought new hope to patients suffering from this formerly untreatable and lethal disease. Importantly, there have been recent reports of success in some clinical studies examining the efficacy of both targeted agents and immunotherapies that show similar response rates in both brain metastases and extracranial disease. While these studies are encouraging, there remains significant room for improvement in the treatment of MBM, given the lack of durable response and the development of resistance to current therapies. Critical questions remain regarding mechanisms that lead to this lack of durable response and development of resistance, and how those mechanisms may differ in systemic sites versus brain metastases. One issue that may not be fully appreciated is that the delivery of several small molecule molecularly-targeted therapies to the brain is often restricted due to active efflux at the blood-brain barrier (BBB) interface. Inadequate local drug concentrations may be partially responsible for the development of unique patterns of resistance at metastatic sites in the brain. It is clear that there can be local, heterogeneous BBB breakdown in MBM, as exemplified by contrast-enhancement on T1-weighted MR imaging. However, it is possible that the successful treatment of MBM with small molecule targeted therapies will depend, in part, on the ability of these therapies to penetrate an intact BBB and reach the protected micro-metastases (so called "sub-clinical" disease) that escape early detection by contrast-enhanced MRI, as well as regions of tumor within MRI-detectable metastases that may have a less compromised BBB. The emergence of resistance in MBM may be related to several diverse, yet interrelated, factors including the distinct microenvironment of the brain and inadequate brain penetration of targeted therapies to specific regions of tumor. The tumor microenvironment has been ascribed to play a key role in steering the course of disease progression, by dictating changes in expression of tumor drivers and resistance-related signaling mechanisms. Therefore, a key issue to consider is how changes in drug delivery, and hence local drug concentrations within a metastatic microenvironment, will influence the development of resistance. Herein we discuss our perspective on several critical questions that focus on many aspects relevant to the treatment of melanoma brain metastases; the answers to which may lead to important advances in the treatment of this devastating disease.

CNS Physicochemical Property Space Shaped by a Diverse Set of Molecules with Experimentally Determined Exposure in the Mouse Brain

Understanding the "limits and boundaries" of the central nervous system (CNS) property space is a critical aspect of modern CNS drug design. Medicinal chemists are often guided by the physicochemical properties of marketed CNS drugs, which are heavily biased toward "traditional" aminergic targets and commonly described as small lipophilic amines. This miniperspective describes the statistical analysis of the calculated physicochemical properties for a diverse set of ligands for mostly "nontraditional" CNS targets and classified as either "brain penetrant" or "peripherally restricted" on the basis of the experimental mouse brain exposure. The results suggested that (a) the physicochemical property space conducive to brain exposure is larger than the one defined by the marketed CNS drugs and (b) the most critical brain exposure determinants are descriptors of the molecular size and hydrogen bond capacity. These findings led to a modified version of the CNS MPO scoring algorithm, termed CNS MPO.v2.