Harnessing the 3D-Beacons Network: A Comprehensive Guide to Accessing and Displaying Protein Structure Data.

Recent advancements in protein structure determination and especially in protein structure prediction techniques have led to the availability of vast amounts of macromolecular structures. However, the accessibility and integration of these structures into scientific workflows are hindered by the lack of standardization among publicly available data resources. To address this issue, we introduced the 3D-Beacons Network, a unified platform that aims to establish a standardized framework for accessing and displaying protein structure data. In this article, we highlight the importance of standardized approaches for accessing protein structure data and showcase the capabilities of 3D-Beacons. We describe four protocols for finding and accessing macromolecular structures from various specialist data resources via 3D-Beacons. First, we describe three scenarios for programmatically accessing and retrieving data using the 3D-Beacons API. Next, we show how to perform sequence-based searches to find structures from model providers. Then, we demonstrate how to search for structures and fetch them directly into a workflow using JalView. Finally, we outline the process of facilitating access to data from providers interested in contributing their structures to the 3D-Beacons Network. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Programmatic access to the 3D-Beacons API Basic Protocol 2: Sequence-based search using the 3D-Beacons API Basic Protocol 3: Accessing macromolecules from 3D-Beacons with JalView Basic Protocol 4: Enhancing data accessibility through 3D-Beacons.

Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies.

Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe - Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.

Identification of Novel and Early Biomarkers for Cisplatin-induced Nephrotoxicity and the Nephroprotective Role of Cimetidine using a Pharmacometabolomic-based Approach Coupled with In Vitro Toxicodynamic Modeling and Simulation.

Cisplatin is widely used for the treatment of various types of cancer. However, cisplatin-induced nephrotoxicity (CIN) is frequently observed in patients receiving cisplatin therapy which poses a challenge in its clinical utility. Currently used clinical biomarkers for CIN are not adequate for early detection of nephrotoxicity, hence there is a need to identify potential early biomarkers in predicting CIN. In the current study, a combination of in vitro toxicodynamic (TD) modeling and untargeted global metabolomics approach was used to identify novel potential metabolite biomarkers for early detection of CIN. In addition, we investigated the protective role of cimetidine (CIM), an inhibitor of the organic cation transporter 2 (OCT2), in suppressing CIN. We first characterized the time-course of nephrotoxic effects of cisplatin (CIS) and the protective effects of CIM in a human pseudo-immortalized renal proximal tubule epithelial cell line (RPTEC), SA7K cell line. Secondly, we used a mathematical cell-level, in vitro TD modeling approach to quantitatively characterize the time-course effects of CIS and CIM as single agents and combination in SA7K cells. Based on the experimental and modeling results, we selected relevant concentrations of CIS and CIM for our metabolomics study. With the help of PCA (Principal Component Analysis) and PLS-DA (Projection to Latent Structure - Discriminate Analysis) analyses, we confirmed global metabolome changes for different groups (CIS, CIM, CIS+CIM vs control) in SA7K cells. Based on the criterion of a p-value ≤ 0.05 and a fold change ≥ 2 or ≤ 0.5, we identified 20 top metabolites that were significantly changed during the early phase i.e. within first 12 h of CIS treatment. Finally, pathway analysis was conducted that revealed the key metabolic pathways that were most impacted in CIN.

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.

The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.

The AlphaFold Protein Structure Database (AlphaFold DB) is a massive digital library of predicted protein structures, with over 214 million entries, marking a 500-times expansion in size since its initial release in 2021. The structures are predicted using Google DeepMind's AlphaFold 2 artificial intelligence (AI) system. Our new report highlights the latest updates we have made to this database. We have added more data on specific organisms and proteins related to global health and expanded to cover almost the complete UniProt database, a primary data resource of protein sequences. We also made it easier for our users to access the data by directly downloading files or using advanced cloud-based tools. Finally, we have also improved how users view and search through these protein structures, making the user experience smoother and more informative. In short, AlphaFold DB has been growing rapidly and has become more user-friendly and robust to support the broader scientific community.

PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank.

While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments, and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.

Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data.

Macromolecular complexes are essential functional units in nearly all cellular processes, and their atomic-level understanding is critical for elucidating and modulating molecular mechanisms. The Protein Data Bank (PDB) serves as the global repository for experimentally determined structures of macromolecules. Structural data in the PDB offer valuable insights into the dynamics, conformation, and functional states of biological assemblies. However, the current annotation practices lack standardised naming conventions for assemblies in the PDB, complicating the identification of instances representing the same assembly. In this study, we introduce a method leveraging resources external to PDB, such as the Complex Portal, UniProt and Gene Ontology, to describe assemblies and contextualise them within their biological settings accurately. Employing the proposed approach, we assigned standard names to over 90% of unique assemblies in the PDB and provided persistent identifiers for each assembly. This standardisation of assembly data enhances the PDB, facilitating a deeper understanding of macromolecular complexes. Furthermore, the data standardisation improves the PDB's FAIR attributes, fostering more effective basic and translational research and scientific education.

PDBImages: a command-line tool for automated macromolecular structure visualization.

SUMMARY: PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types, and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics. AVAILABILITY AND IMPLEMENTATION: PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images.

3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources.

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.

Development and Validation of a Sensitive and Specific LC-MS/MS Method for IWR-1-Endo, a Wnt Signaling Inhibitor: Application to a Cerebral Microdialysis Study.

IWR-1-endo, a small molecule that potently inhibits the Wnt/ß-catenin signaling pathway by stabilizing the AXIN2 destruction complex, can inhibit drug efflux at the blood−brain barrier. To conduct murine cerebral microdialysis research, validated, sensitive, and reliable liquid chromatography−tandem mass spectrometry (LC-MS/MS) methods were used to determine IWR-1-endo concentration in the murine plasma and brain microdialysate. IWR-1-endo and the internal standard (ISTD) dabrafenib were extracted from murine plasma and microdialysate samples by a simple solid-phase extraction protocol performed on an Oasis HLB µElution plate. Chromatographic separation was executed on a Kinetex C18 (100A, 50 × 2.1 mm, 4 µm particle size) column with a binary gradient of water and acetonitrile, each having 0.1% formic acid, pumped at a flow rate of 0.6 mL/min. Detection by mass spectrometry was conducted in the positive selected reaction monitoring ion mode by monitoring mass transitions 410.40 > 344.10 (IWR-1-endo) and 520.40 > 307.20 (ISTD). The validated curve range of IWR-1-endo was 5−1000 ng/mL for the murine plasma method (r2 ≥ 0.99) and 0.5−500 ng/mL for the microdialysate method (r2 ≥ 0.99). The lower limit of quantification (LLOQ) was 5 ng/mL and 0.5 ng/mL for the murine plasma and microdialysate sample analysis method, respectively. Negligible matrix effects were observed in murine plasma and microdialysate samples. IWR-1-endo was extremely unstable in murine plasma. To improve the stability of IWR-1-endo, pH adjustments of 1.5 were introduced to murine plasma and microdialysate samples before sample storage and processing. With pH adjustment of 1.5 to the murine plasma and microdialysate samples, IWR-1-endo was stable across several tested conditions such as benchtop, autosampler, freeze−thaw, and long term at −80 °C. The LC-MS/MS methods were successfully applied to a murine pharmacokinetic and cerebral microdialysis study to characterize the unbound IWR-1-endo exposure in brain extracellular fluid and plasma.

PDBe and PDBe-KB: Providing high-quality, up-to-date and integrated resources of macromolecular structures to support basic and applied research and education.

The archiving and dissemination of protein and nucleic acid structures as well as their structural, functional and biophysical annotations is an essential task that enables the broader scientific community to conduct impactful research in multiple fields of the life sciences. The Protein Data Bank in Europe (PDBe; pdbe.org) team develops and maintains several databases and web services to address this fundamental need. From data archiving as a member of the Worldwide PDB consortium (wwPDB; wwpdb.org), to the PDBe Knowledge Base (PDBe-KB; pdbekb.org), we provide data, data-access mechanisms, and visualizations that facilitate basic and applied research and education across the life sciences. Here, we provide an overview of the structural data and annotations that we integrate and make freely available. We describe the web services and data visualization tools we offer, and provide information on how to effectively use or even further develop them. Finally, we discuss the direction of our data services, and how we aim to tackle new challenges that arise from the recent, unprecedented advances in the field of structure determination and protein structure modeling.

Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology.

The number of unique transmembrane (TM) protein structures doubled in the last four years, which can be attributed to the revolution of cryo-electron microscopy. In addition, AlphaFold2 (AF2) also provided a large number of predicted structures with high quality. However, if a specific protein family is the subject of a study, collecting the structures of the family members is highly challenging in spite of existing general and protein domain-specific databases. Here, we demonstrate this and assess the applicability and usability of automatic collection and presentation of protein structures via the ABC protein superfamily. Our pipeline identifies and classifies transmembrane ABC protein structures using the PFAM search and also aims to determine their conformational states based on special geometric measures, conftors. Since the AlphaFold database contains structure predictions only for single polypeptide chains, we performed AF2-Multimer predictions for human ABC half transporters functioning as dimers. Our AF2 predictions warn of possibly ambiguous interpretation of some biochemical data regarding interaction partners and call for further experiments and experimental structure determination. We made our predicted ABC protein structures available through a web application, and we joined the 3D-Beacons Network to reach the broader scientific community through platforms such as PDBe-KB.

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.

The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

Investigating the Central Nervous System Disposition of Actinomycin D: Implementation and Evaluation of Cerebral Microdialysis and Brain Tissue Measurements Supported by UPLC-MS/MS Quantification.

Actinomycin D is a potent cytotoxic drug against pediatric (and other) tumors that is thought to barely cross the blood-brain barrier. To evaluate its potential applicability for the treatment of patients with central nervous system (CNS) tumors, we established a cerebral microdialysis model in freely moving mice and investigated its CNS disposition by quantifying actinomycin D in cerebral microdialysate, brain tissue homogenate, and plasma. For this purpose, we developed and validated an ultraperformance liquid chromatography-tandem mass spectrometry assay suitable for ultra-sensitive quantification of actinomycin D in the pertinent biological matrices in micro-samples of only 20 µL, with a lower limit of quantification of 0.05 ng/mL. In parallel, we confirmed actinomycin D as a substrate of P-glycoprotein (P-gp) in in vitro experiments. Two hours after intravenous administration of 0.5 mg/kg, actinomycin D reached total brain tissue concentrations of 4.1 ± 0.7 ng/g corresponding to a brain-to-plasma ratio of 0.18 ± 0.03, while it was not detectable in intracerebral microdialysate. This tissue concentration exceeds the concentrations of actinomycin D that have been shown to be effective in in vitro experiments. Elimination of the drug from brain tissue was substantially slower than from plasma, as shown in a brain-to-plasma ratio of approximately 0.53 after 22 h. Because actinomycin D reached potentially effective concentrations in brain tissue in our experiments, the drug should be further investigated as a therapeutic agent in potentially susceptible CNS malignancies, such as ependymoma.

Development and validation of an LC-MS/MS method to quantify the bromodomain and extra-terminal (BET) inhibitor JQ1 in mouse plasma and brain microdialysate: Application to cerebral microdialysis study.

JQ1, is a cell-permeable small-molecule inhibitor of bromodomain and extra-terminal protein (BET) function with reportedly good CNS penetration, however, unbound and pharmacologically active CNS JQ1 exposures have not been characterized. Additionally, no quantitative bioanalytical methods for JQ1 have been described in the literature to support the CNS penetration studies. In the present article, we discuss the development and validation of a sensitive and reliable liquid chromatography-tandem mass spectrometry (LC-MS/MS) quantitative methods to determine JQ1 in mouse plasma and brain microdialysate. JQ1 and the internal standard, dabrafenib (ISTD), were extracted from plasma and microdialysate samples using a simple solid phase extraction protocol performed on an Oasis HLB µElution plate. Chromatographic separation of JQ1 and ISTD was achieved on a reversed phase C12 analytical column with gradient elution profile of mobile phases (MP A: water containing 0.1 % formic acid and MP B: acetonitrile containing 0.1 % formic acid) at a flow rate of 0.6 mL/min. The mass spectrometric detection was performed in the positive MRM ion mode by monitoring the transitions 457.40 > 341.30 (JQ1) and 520.40 > 307.20 (ISTD). The calibration curves demonstrated good linearities over the concentration range of 5-1000 ng/mL for the mouse plasma method (r2 ≥ 0.99) and 0.5-500 ng/mL for the microdialysate method (r2 ≥ 0.99). The experimental limit of quantification obtained was 5 and 0.5 ng/mL for the mouse plasma and microdialysate method, respectively, with the coefficient of variation less than 10 % for the analyte peak area. All the other validation parameters, including intra-and inter-day accuracy and precision, matrix effect, selectivity, carryover effect, and stability, were within the USFDA bioanalytical guidelines acceptance limits. The LC-MS/MS method was successfully applied to a mouse pharmacokinetic and cerebral microdialysis study to characterize the unbound JQ1 exposure in brain extracellular fluid and plasma.

PDBe aggregated API: programmatic access to an integrative knowledge graph of molecular structure data.

SUMMARY: The PDBe aggregated API is an open-access and open-source RESTful API that provides programmatic access to a wealth of macromolecular structural data and their functional and biophysical annotations through 80+ API endpoints. The API is powered by the PDBe graph database (https://pdbe.org/graph-schema), an open-access integrative knowledge graph that can be used as a discovery tool to answer complex biological questions. AVAILABILITY AND IMPLEMENTATION: The PDBe aggregated API provides up-to-date access to the PDBe graph database, which has weekly releases with the latest data from the Protein Data Bank, integrated with updated annotations from UniProt, Pfam, CATH, SCOP and the PDBe-KB partner resources. The complete list of all the available API endpoints and their descriptions are available at https://pdbe.org/graph-api. The source code of the Python 3.6+ API application is publicly available at https://gitlab.ebi.ac.uk/pdbe-kb/services/pdbe-graph-api. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Facilitative lysosomal transport of bile acids alleviates ER stress in mouse hematopoietic precursors.

Mutations in human equilibrative nucleoside transporter 3 (ENT3) encoded by SLC29A3 results in anemia and erythroid hypoplasia, suggesting that ENT3 may regulate erythropoiesis. Here, we demonstrate that lysosomal ENT3 transport of taurine-conjugated bile acids (TBA) facilitates TBA chemical chaperone function and alleviates endoplasmic reticulum (ER) stress in expanding mouse hematopoietic stem and progenitor cells (HSPCs). Slc29a3-/- HSPCs accumulate less TBA despite elevated levels of TBA in Slc29a3-/- mouse plasma and have elevated basal ER stress, reactive oxygen species (ROS), and radiation-induced apoptosis. Reintroduction of ENT3 allows for increased accumulation of TBA into HSPCs, which results in TBA-mediated alleviation of ER stress and erythroid apoptosis. Transplanting TBA-preconditioned HSPCs expressing ENT3 into Slc29a3-/- mice increase bone marrow repopulation capacity and erythroid pool size and prevent early mortalities. Together, these findings suggest a putative role for a facilitative lysosomal transporter in the bile acid regulation of ER stress in mouse HSPCs which may have implications in erythroid biology, the treatment of anemia observed in ENT3-mutated human genetic disorders, and nucleoside analog drug therapy.

Exposure-Toxicity Association of Cyclophosphamide and Its Metabolites in Infants and Young Children with Primary Brain Tumors: Implications for Dosing.

PURPOSE: To characterize the population pharmacokinetics of cyclophosphamide, active 4-hydroxy-cyclophosphamide (4OH-CTX), and inactive carboxyethylphosphoramide mustard (CEPM), and their associations with hematologic toxicities in infants and young children with brain tumors. To use this information to provide cyclophosphamide dosing recommendations in this population. PATIENTS AND METHODS: Patients received four cycles of a 1-hour infusion of 1.5 g/m2 cyclophosphamide. Serial samples were collected to measure cyclophosphamide, 4OH-CTX, and CEPM plasma concentrations. Population pharmacokinetic modeling was performed to identify the patient characteristics influencing drug disposition. Associations between drug exposures and metrics reflecting drug-induced neutropenia, erythropenia, and thrombocytopenia were investigated. A Bayesian approach was developed to predict 4OH-CTX exposure using only cyclophosphamide and CEPM plasma concentrations. RESULTS: Data from 171 patients (0.07-4.9 years) were adequately fitted by a two-compartment (cyclophosphamide) and one-compartment model (metabolites). Young infants (<6 months) exhibited higher mean 4OH-CTX exposure than did young children (138.4 vs. 107.2 µmol/L·h, P < 0.0001). No genotypes exhibited clinically significant influence on drug exposures. Worse toxicity metrics were significantly associated with higher 4OH-CTX exposures. Dosing simulations suggested decreased cyclophosphamide dosage to 1.2 g/m2 for young infants versus 1.5 g/m2 for children to attain similar 4OH-CTX exposure. Bayesian-modeled 4OH-CTX exposure predictions were precise (mean absolute prediction error 14.8% ± 4.2%) and had low bias (mean prediction error 4.9% ± 5.1%). CONCLUSIONS: A 4OH-CTX exposure-toxicity association was established, and a decreased cyclophosphamide dosage for young infants was suggested to reduce toxicity in this population. Bayesian modeling to predict 4OH-CTX exposure may reduce clinical processing-related costs and provide insights into further exposure-response associations.

PDBe: improved findability of macromolecular structure data in the PDB.

The Protein Data Bank in Europe (PDBe), a founding member of the Worldwide Protein Data Bank (wwPDB), actively participates in the deposition, curation, validation, archiving and dissemination of macromolecular structure data. PDBe supports diverse research communities in their use of macromolecular structures by enriching the PDB data and by providing advanced tools and services for effective data access, visualization and analysis. This paper details the enrichment of data at PDBe, including mapping of RNA structures to Rfam, and identification of molecules that act as cofactors. PDBe has developed an advanced search facility with ∼100 data categories and sequence searches. New features have been included in the LiteMol viewer at PDBe, with updated visualization of carbohydrates and nucleic acids. Small molecules are now mapped more extensively to external databases and their visual representation has been enhanced. These advances help users to more easily find and interpret macromolecular structure data in order to solve scientific problems.

CNS Penetration of Cyclophosphamide and Metabolites in Mice Bearing Group 3 Medulloblastoma and Non-Tumor Bearing Mice.

PURPOSE: Cyclophosphamide is widely used to treat children with medulloblastoma; however, little is known about its brain penetration. We performed cerebral microdialysis to characterize the brain penetration of cyclophosphamide (130 mg/kg, IP) and its metabolites [4-hydroxy-cyclophosphamide (4OH-CTX) and carboxyethylphosphoramide mustard (CEPM)] in non-tumor bearing mice and mice bearing orthotopic Group 3 medulloblastoma. METHODS: A plasma pharmacokinetic study was performed in non-tumor-bearing CD1- nude mice, and four cerebral microdialysis studies were performed in non-tumor-bearing (M1 and M3) and tumor- bearing mice (M2 and M4). Plasma samples were collected up to 6-hours post-dose, and extracellular fluid (ECF) samples were collected over 60-minute intervals for 24-hours post-dose. To stabilize and quantify 4OH-CTX, a derivatizing solution was added in blood after collection, and either directly in the microdialysis perfusate (M1 and M2) or in ECF collection tubes (M3 and M4). Plasma/ECF cyclophosphamide and CEPM, and 4OH-CTX concentrations were separately measured using different LC-MS/MS methods. RESULTS: All plasma/ECF concentrations were described using a population-based pharmacokinetic model. Plasma exposures of cyclophosphamide, 4OH-CTX, and CEPM were similar across studies (mean AUC=112.6, 45.6, and 80.8 µmol∙hr/L). Hemorrhage was observed in brain tissue when the derivatizing solution was in perfusate compared with none when in collection tubes, which suggested potential sample contamination in studies M1 and M2. Model-derived unbound ECF to plasma partition coefficients (Kp,uu) were calculated to reflect CNS penetration of the compounds. Lower cyclophosphamide Kp,uu was obtained in tumor-bearing mice versus non-tumor bearing mice (mean 0.15 versus 0.22, p=0.019). No differences in Kp,uu were observed between these groups for 4OH- CTX and CEPM (overall mean 0.10 and 0.07). CONCLUSIONS: Future studies will explore potential mechanisms at the brain-tumor barrier to explain lower cyclophosphamide brain penetration in tumor-bearing mice. These results will be used to further investigate exposure-response relationships in medulloblastoma xenograft models.