A systematic review of kidney-on-a-chip-based models to study human renal (patho-)physiology.

As kidney diseases affect ∼10% of the world population, understanding the underlying mechanisms and developing therapeutic interventions are of high importance. Although animal models have enhanced knowledge of disease mechanisms, human (patho-)physiology may not be adequately represented in animals. Developments in microfluidics and renal cell biology have enabled the development of dynamic models to study renal (patho-)physiology in vitro. Allowing inclusion of human cells and combining different organ models, such as kidney-on-a-chip (KoC) models, enable the refinement and reduction of animal experiments. We systematically reviewed the methodological quality, applicability and effectiveness of kidney-based (multi-)organ-on-a-chip models, and describe the state-of-the-art, strengths and limitations, and opportunities regarding basic research and implementation of these models. We conclude that KoC models have evolved to complex models capable of mimicking systemic (patho-)physiological processes. Commercial chips and human induced pluripotent stem cells and organoids are important for KoC models to study disease mechanisms and assess drug effects, even in a personalized manner. This contributes to the Reduction, Refinement and Replacement of animal models for kidney research. A lack of reporting of intra- and inter-laboratory reproducibility and translational capacity currently hampers implementation of these models.

Considering developmental neurotoxicity in vitro data for human health risk assessment using physiologically-based kinetic modeling: deltamethrin case study.

Developmental neurotoxicity (DNT) is a potential hazard of chemicals. Recently, an in vitro testing battery (DNT IVB) was established to complement existing rodent in vivo approaches. Deltamethrin (DLT), a pyrethroid with a well-characterized neurotoxic mode of action, has been selected as a reference chemical to evaluate the performance of the DNT IVB. The present study provides context for evaluating the relevance of these DNT IVB results for the human health risk assessment of DLT by estimating potential human fetal brain concentrations after maternal exposure to DLT. We developed a physiologically based kinetic (PBK) model for rats which was then translated to humans considering realistic in vivo exposure conditions (acceptable daily intake [ADI] for DLT). To address existing uncertainties, we designed case studies considering the most relevant drivers of DLT uptake and distribution. Calculated human fetal brain concentrations were then compared with the lowest benchmark concentration achieved in the DNT IVB. The developed rat PBK model was validated on in vivo rat toxicokinetic data of DLT over a broad range of doses. The uncertainty based case study evaluation confirmed that repeated exposure to DLT at an ADI level would likely result in human fetal brain concentrations far below the in vitro benchmark. The presented results indicate that DLT concentrations in the human fetal brain are highly unlikely to reach concentrations associated with in vitro findings under realistic exposure conditions. Therefore, the new in vitro DNT results are considered to have no impact on the current risk assessment approach.

Studying metabolism with multi-organ chips: new tools for disease modelling, pharmacokinetics and pharmacodynamics.

Non-clinical models to study metabolism including animal models and cell assays are often limited in terms of species translatability and predictability of human biology. This field urgently requires a push towards more physiologically accurate recapitulations of drug interactions and disease progression in the body. Organ-on-chip systems, specifically multi-organ chips (MOCs), are an emerging technology that is well suited to providing a species-specific platform to study the various types of metabolism (glucose, lipid, protein and drug) by recreating organ-level function. This review provides a resource for scientists aiming to study human metabolism by providing an overview of MOCs recapitulating aspects of metabolism, by addressing the technical aspects of MOC development and by providing guidelines for correlation with in silico models. The current state and challenges are presented for two application areas: (i) disease modelling and (ii) pharmacokinetics/pharmacodynamics. Additionally, the guidelines to integrate the MOC data into in silico models could strengthen the predictive power of the technology. Finally, the translational aspects of metabolizing MOCs are addressed, including adoption for personalized medicine and prospects for the clinic. Predictive MOCs could enable a significantly reduced dependence on animal models and open doors towards economical non-clinical testing and understanding of disease mechanisms.

Modelling human liver fibrosis in the context of non-alcoholic steatohepatitis using a microphysiological system.

Non-alcoholic steatohepatitis (NASH) is a common form of chronic liver disease characterised by lipid accumulation, infiltration of immune cells, hepatocellular ballooning, collagen deposition and liver fibrosis. There is a high unmet need to develop treatments for NASH. We have investigated how liver fibrosis and features of advanced clinical disease can be modelled using an in vitro microphysiological system (MPS). The NASH MPS model comprises a co-culture of primary human liver cells, which were cultured in a variety of conditions including+/- excess sugar, fat, exogenous TGFß or LPS. The transcriptomic, inflammatory and fibrotic phenotype of the model was characterised and compared using a system biology approach to identify conditions that mimic more advanced clinical disease. The transcriptomic profile of the model was shown to closely correlate with the profile of patient samples and the model displayed a quantifiable fibrotic phenotype. The effects of Obeticholic acid and Elafibranor, were evaluated in the model, as wells as the effects of dietary intervention, with all able to significantly reduce inflammatory and fibrosis markers. Overall, we demonstrate how the MPS NASH model can be used to model different aspects of clinical NASH but importantly demonstrate its ability to model advanced disease with a quantifiable fibrosis phenotype.

Minimal brain PBPK model to support the preclinical and clinical development of antibody therapeutics for CNS diseases.

There are several antibody therapeutics in preclinical and clinical development, industry-wide, for the treatment of central nervous system (CNS) disorders. Due to the limited permeability of antibodies across brain barriers, the quantitative understanding of antibody exposure in the CNS is important for the design of antibody drug characteristics and determining appropriate dosing regimens. We have developed a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain for antibody therapeutics, which was reduced from an existing multi-species platform brain PBPK model. All non-brain compartments were combined into a single tissue compartment and cerebral spinal fluid (CSF) compartments were combined into a single CSF compartment. The mPBPK model contains 16 differential equations, compared to 100 in the original PBPK model, and improved simulation speed approximately 11-fold. Area under the curve ratios for minimal versus full PBPK models were close to 1 across species for both brain and plasma compartments, which indicates the reduced model simulations are similar to those of the original model. The minimal model retained detailed physiological processes of the brain while not significantly affecting model predictability, which supports the law of parsimony in the context of balancing model complexity with added predictive power. The minimal model has a variety of applications for supporting the preclinical development of antibody therapeutics and can be expanded to include target information for evaluating target engagement to inform clinical dose selection.

Gut-Liver Physiomimetics Reveal Paradoxical Modulation of IBD-Related Inflammation by Short-Chain Fatty Acids.

Although the association between the microbiome and IBD and liver diseases is known, the cause and effect remain elusive. By connecting human microphysiological systems of the gut, liver, and circulating Treg and Th17 cells, we created a multi-organ model of ulcerative colitis (UC) ex vivo. The approach shows microbiome-derived short-chain fatty acids (SCFAs) to either improve or worsen UC severity, depending on the involvement of effector CD4 T cells. Using multiomics, we found SCFAs increased production of ketone bodies, glycolysis, and lipogenesis, while markedly reducing innate immune activation of the UC gut. However, during acute T cell-mediated inflammation, SCFAs exacerbated CD4+ T cell-effector function, partially through metabolic reprograming, leading to gut barrier disruption and hepatic injury. These paradoxical findings underscore the emerging utility of human physiomimetic technology in combination with systems immunology to study causality and the fundamental entanglement of immunity, metabolism, and tissue homeostasis.

Translational Assessment of Drug-Induced Proximal Tubule Injury Using a Kidney Microphysiological System.

Drug-induced kidney injury, a major cause of acute kidney injury, results in progressive kidney disease and is linked to increased mortality in hospitalized patients. Primary injury sites of drug-induced kidney injury are proximal tubules. Clinically, kidney injury molecule-1, an established tubule-specific biomarker, is monitored to assess the presence and progression of injury. The ability to accurately predict drug-related nephrotoxicity preclinically would reduce patient burden and drug attrition rates, yet state-of-the-art in vitro and animal models fail to do so. In this study, we demonstrate the use of kidney injury molecule-1 measurement in the kidney microphysiological system as a preclinical model for drug toxicity assessment. To show clinical relevance, we use quantitative systems pharmacology computational models for in vitro-in vivo translation of the experimental results and to identify favorable dosing regimens for one of the tested drugs.

Benchmarking lithium amide versus amine bonding by charge density and energy decomposition analysis arguments.

Lithium amides are versatile C-H metallation reagents with vast industrial demand because of their high basicity combined with their weak nucleophilicity, and they are applied in kilotons worldwide annually. The nuclearity of lithium amides, however, modifies and steers reactivity, region- and stereo-selectivity and product diversification in organic syntheses. In this regard, it is vital to understand Li-N bonding as it causes the aggregation of lithium amides to form cubes or ladders from the polar Li-N covalent metal amide bond along the ring stacking and laddering principle. Deaggregation, however, is more governed by the Li←N donor bond to form amine adducts. The geometry of the solid state structures already suggests that there is σ- and π-contribution to the covalent bond. To quantify the mutual influence, we investigated [{(Me2NCH2)2(C4H2N)}Li]2 (1) by means of experimental charge density calculations based on the quantum theory of atoms in molecules (QTAIM) and DFT calculations using energy decomposition analysis (EDA). This new approach allows for the grading of electrostatic Li+N-, covalent Li-N and donating Li←N bonding, and provides a way to modify traditional widely-used heuristic concepts such as the -I and +I inductive effects. The electron density ρ(r) and its second derivative, the Laplacian ∇2ρ(r), mirror the various types of bonding. Most remarkably, from the topological descriptors, there is no clear separation of the lithium amide bonds from the lithium amine donor bonds. The computed natural partial charges for lithium are only +0.58, indicating an optimal density supply from the four nitrogen atoms, while the Wiberg bond orders of about 0.14 au suggest very weak bonding. The interaction energy between the two pincer molecules, (C4H2N)22-, with the Li22+ moiety is very strong (ca. -628 kcal mol-1), followed by the bond dissociation energy (-420.9 kcal mol-1). Partitioning the interaction energy into the Pauli (ΔEPauli), dispersion (ΔEdisp), electrostatic (ΔEelstat) and orbital (ΔEorb) terms gives a 71-72% ionic and 25-26% covalent character of the Li-N bond, different to the old dichotomy of 95 to 5%. In this regard, there is much more potential to steer the reactivity with various substituents and donor solvents than has been anticipated so far.

Establishing quasi-steady state operations of microphysiological systems (MPS) using tissue-specific metabolic dependencies.

Microphysiological systems (MPS), consisting of tissue constructs, biomaterials, and culture media, aim to recapitulate relevant organ functions in vitro. MPS components are housed in fluidic hardware with operational protocols, such as periodic complete media replacement. Such batch-like operations provide relevant nutrients and remove waste products but also reset cell-secreted mediators (e.g. cytokines, hormones) and potentially limit exposure to drugs (and metabolites). While each component plays an essential role for tissue functionality, MPS-specific nutrient needs are not yet well-characterized nor utilized to operate MPSs at more physiologically-relevant conditions. MPS-specific nutrient needs for gut (immortalized cancer cells), liver (human primary hepatocytes) and cardiac (iPSC-derived cardiomyocytes) MPSs were experimentally quantified. In a long-term study of the gut MPS (10 days), this knowledge was used to design operational protocols to maintain glucose and lactate at desired levels. This quasi-steady state operation was experimentally validated by monitoring glucose and lactate as well as MPS functionality. In a theoretical study, nutrient needs of an integrated multi-MPS platform (gut, liver, cardiac MPSs) were computationally simulated to identify long-term quasi-steady state operations. This integrative experimental and computational approach demonstrates the utilization of quantitative multi-scale characterization of MPSs and incorporating MPS-specific information to establish more physiologically-relevant experimental operations.

Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies.

Microphysiological systems (MPSs) are in vitro models that capture facets of in vivo organ function through use of specialized culture microenvironments, including 3D matrices and microperfusion. Here, we report an approach to co-culture multiple different MPSs linked together physiologically on re-useable, open-system microfluidic platforms that are compatible with the quantitative study of a range of compounds, including lipophilic drugs. We describe three different platform designs - "4-way", "7-way", and "10-way" - each accommodating a mixing chamber and up to 4, 7, or 10 MPSs. Platforms accommodate multiple different MPS flow configurations, each with internal re-circulation to enhance molecular exchange, and feature on-board pneumatically-driven pumps with independently programmable flow rates to provide precise control over both intra- and inter-MPS flow partitioning and drug distribution. We first developed a 4-MPS system, showing accurate prediction of secreted liver protein distribution and 2-week maintenance of phenotypic markers. We then developed 7-MPS and 10-MPS platforms, demonstrating reliable, robust operation and maintenance of MPS phenotypic function for 3 weeks (7-way) and 4 weeks (10-way) of continuous interaction, as well as PK analysis of diclofenac metabolism. This study illustrates several generalizable design and operational principles for implementing multi-MPS "physiome-on-a-chip" approaches in drug discovery.

Multi-functional scaling methodology for translational pharmacokinetic and pharmacodynamic applications using integrated microphysiological systems (MPS).

Microphysiological systems (MPS) provide relevant physiological environments in vitro for studies of pharmacokinetics, pharmacodynamics and biological mechanisms for translational research. Designing multi-MPS platforms is essential to study multi-organ systems. Typical design approaches, including direct and allometric scaling, scale each MPS individually and are based on relative sizes not function. This study's aim was to develop a new multi-functional scaling approach for integrated multi-MPS platform design for specific applications. We developed an optimization approach using mechanistic modeling and specification of an objective that considered multiple MPS functions, e.g., drug absorption and metabolism, simultaneously to identify system design parameters. This approach informed the design of two hypothetical multi-MPS platforms consisting of gut and liver (multi-MPS platform I) and gut, liver and kidney (multi-MPS platform II) to recapitulate in vivo drug exposures in vitro. This allows establishment of clinically relevant drug exposure-response relationships, a prerequisite for efficacy and toxicology assessment. Design parameters resulting from multi-functional scaling were compared to designs based on direct and allometric scaling. Human plasma time-concentration profiles of eight drugs were used to inform the designs, and profiles of an additional five drugs were calculated to test the designed platforms on an independent set. Multi-functional scaling yielded exposure times in good agreement with in vivo data, while direct and allometric scaling approaches resulted in short exposure durations. Multi-functional scaling allows appropriate scaling from in vivo to in vitro of multi-MPS platforms, and in the cases studied provides designs that better mimic in vivo exposures than standard MPS scaling methods.

Physiologically based pharmacokinetic modeling of (18)F-SiFAlin-Asp3-PEG1-TATE in AR42J tumor bearing mice.

PURPOSE: Peptide receptor radionuclide therapy (PRRT) is commonly performed in the treatment of neuroendocrine tumors (NET), where somatostatin analogs (DOTATATE) are radiolabeled with (90)Y, (68)Ga or (111)In for pre-therapeutic and therapeutic purposes. Quantitative evaluation of the biokinetic data can be performed by using physiologically based pharmacokinetic (PBPK) models. Knowledge about the biodistribution in a pre-clinical setting would allow optimizing the translation from bench to bedside. The aim of this study was to develop a PBPK model to describe the biodistribution of a novel sst2-targeting radiotracer. METHODS: Biokinetic data of six mice after injection of (18)F-SiFAlin-Asp3-PEG1-TATE were investigated using two PBPK models. The PBPK models describe the biodistribution of the tracer in the tumor, kidneys, liver, remainder and whole body via blood flow to these organs via absorption, distribution, metabolism and excretion. A recently published sst2 PBPK model for humans (model 1) was used to describe the data. Physiological information in this model was adapted to that of a mouse. Model 1 was further modified by implementing receptor-mediated endocytosis (model 2). Model parameters were fitted to the biokinetic data of each mouse. Model selection was performed by calculating Akaike weights wi using the corrected Akaike Information Criterion (AICc). RESULTS: The implementation of receptor-mediated endocytosis considerably improved the description of the biodistribution (Akaike weights w1=0% and w2=100% for model 1 and 2, respectively). The resulting time-integrated activity coefficients determined by model 2 were for tumor (0.05 ± 0.02) h, kidneys (0.11 ± 0.01) h and liver (0.02 ± 0.01) h. CONCLUSION: Simply downscaling a human PBPK model does not allow for an accurate description of (18)F-SiFAlin-Asp3-PEG1-TATE in mice. Biokinetics of this tracer can be accurately and adequately described using a physiologically based pharmacokinetic model including receptor-mediated endocytosis. Thus, an optimized translation from bench to bedside is possible.

Dependence of treatment planning accuracy in peptide receptor radionuclide therapy on the sampling schedule.

BACKGROUND: Peptide receptor radionuclide therapy (PRRT) plays an important role in the treatment of neuroendocrine tumors (NET). Pre-therapeutic dosimetry using the area under the measured time-activity curve (AUC) is important. The sampling schedule for this dosimetry determines the accuracy and reliability of the obtained AUC. The aim of this study was to investigate the effect of reduced number of measurement points (i.e., gamma camera image acquisition or serum measurements) on treatment planning accuracy in PRRT using (111)In-labeled-diethylenetriaminopentaacetic acid-octreotide (DTPAOC; Octreoscan™). METHODS: Pre-therapeutic biokinetic data of 15 NET patients were investigated using a recently developed physiologically based pharmacokinetic (PBPK) model. Two parameter sets were determined (standard or iterative approach) and used for calculation of time-integrated activity coefficients (TIACs) for the tumor, kidneys, liver, spleen, serum, and whole body. TIACs obtained using the full data sets were used as reference. To evaluate the effect of sampling on individual treatment planning, reduced sampling schedules were generated omitting either 1, 2, 3, or 4 organ and serum measurements or all serum measurements for each patient. Relative deviations (RDs) between these and reference TIACs were calculated and used as criterion for treatment planning accuracy. An RD < 0.1 was considered acceptable. RESULTS: When omitting serum measurements, TIAC accuracy remained acceptable (RD < 0.1) for the standard approach. The kidney TIACs could be estimated for both approaches with acceptable RDs using two time points (t = 4 h; 2 d); tumor RDs were <0.3. The iterative approach reduced the range of RD, but did not further reduce the number of needed measurement points (i.e., to achieve an RD <0.1). For both approaches RDs for liver, spleen and whole body were larger than 0.1. However, in the clinical setting these RDs are less relevant as liver and spleen are not organs at risk due to the low absorbed doses. CONCLUSIONS: When using a priori information of a PBPK model structure combined with Bayesian information about PBPK model parameter distribution, the administered activity could be determined with acceptable accuracy using only two time points (4 h, 2 d) and thus allow a considerable reduction of needed data for individual dosimetry.

The role of patient-based treatment planning in peptide receptor radionuclide therapy.

BACKGROUND: Accurate treatment planning is recommended in peptide-receptor radionuclide therapy (PRRT) to minimize the toxicity to organs at risk while maximizing tumor cell sterilization. The aim of this study was to quantify the effect of different degrees of individualization on the prediction accuracy of individual therapeutic biodistributions in patients with neuroendocrine tumors (NETs). METHODS: A recently developed physiologically based pharmacokinetic (PBPK) model was fitted to the biokinetic data of 15 patients with NETs after pre-therapeutic injection of (111)In-DTPAOC. Mathematical phantom patients (MPP) were defined using the assumed true (true MPP), mean (MPP 1A) and median (MPP 1B) parameter values of the patient group. Alterations of the degree of individualization were introduced to both mean and median patients by including patient-specific information as a priori knowledge: physical parameters and hematocrit (MPP 2A/2B). Successively, measurable individual biokinetic parameters were added: tumor volume V tu (MPP 3A/3B), glomerular filtration rate GFR (MPP 4A/4B), and tumor perfusion f tu (MPP 5A/5B). Furthermore, parameters of MPP 5A/5B and a simulated (68)Ga-DOTATATE PET measurement 60 min p.i. were used together with the population values used as Bayesian parameters (MPP 6A/6B). Therapeutic biodistributions were simulated assuming an infusion of (90)Y-DOTATATE (3.3 GBq) over 30 min to all MPPs. Time-integrated activity coefficients were predicted for all MPPs and compared to the true MPPs for each patient in tumor, kidneys, spleen, liver, remainder, and whole body to obtain the relative differences RD. RESULTS: The large RD values of MPP 1A [RDtumor = (625 ± 1266)%, RDkidneys = (11 ± 38)%], and MPP 1B [RDtumor = (197 ± 505)%, RDkidneys = (11 ± 39)%] demonstrate that individual treatment planning is needed due to large physiological differences between patients. Although addition of individual patient parameters reduced the deviations considerably [MPP 5A: RDtumor = (-2 ± 27)% and RDkidneys = (16 ± 43)%; MPP 5B: RDtumor = (2 ± 28)% and RDkidneys = (7 ± 40)%] errors were still large. For the kidneys, prediction accuracy was considerably improved by including the PET measurement [MPP 6A/MPP 6B: RDtumor = (-2 ± 22)% and RDkidneys = (-0.1 ± 0.5)%]. CONCLUSION: Individualized treatment planning is needed in the investigated patient group. The use of a PBPK model and the inclusion of patient specific data, e.g., weight, tumor volume, and glomerular filtration rate, do not suffice to predict the therapeutic biodistribution. Integrating all available a priori information in the PBPK model and using additionally PET data measured at one time point for tumor, kidneys, spleen, and liver could possibly be sufficient to perform an individualized treatment planning.

Optimized Peptide Amount and Activity for 9°Y-Labeled DOTATATE Therapy.

UNLABELLED: In peptide receptor radionuclide therapy with (90)Y-labeled DOTATATE, the kidney absorbed dose limits the maximum amount of total activity that can be safely administered in many patients. A higher tumor-to-kidney absorbed dose ratio might be achieved by optimizing the amount of injected peptide and activity, as recent studies have shown different degrees of receptor saturation for normal tissue and tumor. The aim of this work was to develop and implement a modeling method for treatment planning to determine the optimal combination of peptide amount and pertaining therapeutic activity for each patient. METHODS: A whole-body physiologically based pharmacokinetic (PBPK) model was developed. General physiologic parameters were taken from the literature. Individual model parameters were fitted to a series (n= 12) of planar γ-camera and serum measurements ((111)In-DOTATATE) of patients with meningioma or neuroendocrine tumors (NETs). Using the PBPK model and the individually estimated parameters, we determined the tumor, liver, spleen, and red marrow biologically effective doses (BEDs) for a maximal kidney BED (20 Gy2.5) for different peptide amounts and activities. The optimal combination of peptide amount and activity for maximal tumor BED, considering the additional constraint of a red marrow BED less than 1 Gy15, was individually quantified. RESULTS: The PBPK model describes the biokinetic data well considering the criteria of visual inspection, the coefficients of determination, the relative standard errors (<50%), and the correlation of the parameters (<0.8). All fitted parameters were in a physiologically reasonable range but varied considerably between patients, especially tumor perfusion (meningioma, 0.1-1 mL·g(-1)·min(-1), and NETs, 0.02-1 mL·g(-1)·min(-1)) and receptor density (meningioma, 5-34 nmol·L(-1), and NETs, 7-35 nmol·L(-1)). Using the proposed method, we identified the optimal amount and pertaining activity to be 76 ± 46 nmol (118 ± 71 µg) and 4.2 ± 1.8 GBq for meningioma and 87 ± 50 nmol (135 ± 78 µg) and 5.1 ± 2.8 GBq for NET patients. CONCLUSION: The presented work suggests that to achieve higher efficacy and safety for (90)Y-DOATATE therapy, both the administered amount of peptide and the activity should be optimized in treatment planning using the proposed method. This approach could also be adapted for therapy with other peptides.

Population-Based Modeling Improves Treatment Planning Before (90)Y-Labeled Anti-CD66 Antibody Radioimmunotherapy.

For treatment planning in radioimmunotherapy (RIT), the accurate estimation of time-integrated activity coefficients (TIACs) is essential. To estimate the TIACs in RIT using (90)Y-labeled anti-CD66 antibodies, physiologically based pharmacokinetic (PBPK) models are advantageous. Further optimization in predicting therapeutic TIACs may be achieved by including population-specific parameters. Therefore, the aims of this work were (1) to estimate population parameters and (2) to show the effect of these parameters on prediction accuracy of therapeutic biodistributions. To estimate population values, a PBPK model was fitted to pretherapeutic (gamma camera and serum) and therapeutic (serum) measurements simultaneously using the standard two-stage (STS) and iterated two-stage (ITS) algorithms. Including the estimated population values as Bayesian information, the model parameters of each patient were fitted to pretherapeutic data only (simulating therapeutic TIACs). To validate the prediction accuracy of the therapeutic serum curve, the simulated and fitted TIACs were compared. Prediction accuracy expressed as relative deviation (RD) improved from RD=8%±16% to RD=0%±10% for STS and ITS, respectively. The authors demonstrated a method to estimate and apply population values for RIT using a PBPK model and population fitting. For (90)Y-labeled anti-CD66 antibodies, the prediction accuracy was substantially improved.

Physiologically Based Pharmacokinetic Modeling Is Essential in 90Y-Labeled Anti-CD66 Radioimmunotherapy.

INTRODUCTION: Radioimmunotherapy (RIT) with 90Y-labeled anti-CD66 antibody is used to selectively irradiate the red marrow (RM) before blood stem cell transplantation of acute leukemia patients. To calculate the activity to administer, time-integrated activity coefficients are required. These are estimated prior to therapy using gamma camera and serum measurements after injection of 111In labeled anti-CD66 antibody. Equal pre-therapeutic and therapeutic biodistributions are usually assumed to calculate the coefficients. However, additional measurements during therapy had shown that this assumption had to be abandoned. A physiologically based pharmacokinetic (PBPK) model was developed to allow the prediction of therapeutic time-integrated activity coefficients in eight patients. AIMS: The aims of the study were to demonstrate using a larger patient group 1) the need to perform patient-specific dosimetry in 90Y-labeled anti-CD66 RIT, 2) that pre-therapeutic and therapeutic biodistributions differ, and most importantly 3) that this difference in biodistributions can be accurately predicted using a refined model. MATERIALS AND METHODS: Two new PBPK models were developed considering fully, half and non-immunoreactive antibodies and constraints for estimating the RM antigen number. Both models were fitted to gamma camera and serum measurements of 27 patients. Akaike weights were used for model averaging. Time-integrated activity coefficients for total body, liver, spleen, RM and serum were calculated. Model-based predictions of the serum biokinetics during therapy were compared to actual measurements. RESULTS: Variability of the RM time-integrated activity coefficients ((37.3 ± 7.5) h) indicates the need for patient-specific dosimetry. The relative differences between pre-therapeutic and therapeutic serum time-activity curves were (-25 ± 16)%. The prediction accuracy of these differences using the refined PBPK models was (-3 ± 20)%. CONCLUSION: Individual treatment is needed due to biological differences between patients in RIT with 90Y-labeled anti-CD66 antibody. Differences in pre-therapeutic and therapeutic biokinetics are predominantly caused by different degrees of saturation due to different amounts of administered antibody. These differences could be predicted using the PBPK models.

Formal anti-carbopalladation reactions of non-activated alkynes: requirements, mechanistic insights, and applications.

Formal anti-carbopalladation reactions of CC triple bonds are uncommon, but highly useful transformations. Alkynes can be designed to give anti-carbopalladation products. Prerequisite is the exclusion of other reaction pathways to provoke the cis-trans isomerization of the syn-carbopalladation intermediate. Detailed mechanistic studies of this crucial step by experimental and computational means were performed. Application of an intramolecular version for the synthesis of oligocyclic compounds and substituted dibenzofurans is also described.

Effects of metal coordination on the π-system of the 2,5-bis-{(pyrrolidino)-methyl}-pyrrole pincer ligand.

Pincer complexes of 2,5-bis{(pyrrolidino)-methyl}-pyrrole with group 14 elements such as germanium, tin, and lead were prepared and fully characterized by X-ray single-crystal analysis, NMR spectroscopy, and mass spectrometry. The structures of the complexes were analyzed and compared to the free and the lithiated ligand to gain insight into the effects of metal coordination on the aromatic system. A further aspect was to elaborate the capability of group 14 metals to interact with the pyrrole π-system. Therefore, electronic structure calculations were carried out with group 14 complexes to better understand the bonding situation and the trends among the group. The changes in the aromaticity of the pyrrole ring upon coordination have been rationalized according to the interaction of the π-system with the metal. The unusual short bond distance observed between germanium and the coordinated pyrrole nitrogen was also assessed.