Unlabeled Drug Research Articles

MALDI Imaging Mass Spectrometry Visualizes the Distribution of Antidepressant Duloxetine and Its Major Metabolites in Mouse Brain, Liver, Kidney, and Spleen Tissues.

Imaging mass spectrometry (IMS) is a powerful tool for mapping the spatial distribution of unlabeled drugs and metabolites that may find application in assessing drug delivery, explaining drug efficacy, and identifying potential toxicity. This study focuses on determining the spatial distribution of the antidepressant duloxetine, which is widely prescribed despite common adverse effects (liver injury, constant headaches) whose mechanisms are not fully understood. We used high-resolution IMS with matrix-assisted laser desorption/ionization to examine the distribution of duloxetine and its major metabolites in four mouse organs where it may contribute to efficacy or toxicity: brain, liver, kidney, and spleen. In none of these tissues is duloxetine or its metabolites homogeneously distributed, which has implications for both efficacy and toxicity. We found duloxetine to be similarly distributed in spleen red pulp and white pulp but differentially distributed in different anatomic regions of the liver, kidney, and brain, with dose-dependent patterns. Comparison with hematoxylin and eosin staining of tissue sections reveals that the ion images of endogenous lipids help delineate anatomic regions in the brain and kidney, while heme ion images assist in differentiating regions within the spleen. These endogenous metabolites may serve as a valuable resource for examining the spatial distribution of other drugs in tissues when staining images are not available. These findings may facilitate future mechanistic studies of the therapeutic and adverse effects of duloxetine. In the current work, we did not perform absolute quantification of duloxetine, which will be reported in due course. SIGNIFICANCE STATEMENT: The study utilized imaging mass spectrometry to examine the spatial distribution of duloxetine and its primary metabolites in mouse brain, liver, kidney, and spleen. These results may pave the way for future investigations into the mechanisms behind duloxetine's therapeutic and adverse effects. Furthermore, the mass spectrometry images of specific endogenous metabolites such as heme could be valuable in analyzing the spatial distribution of other drugs within tissues in scenarios where histological staining images are unavailable.

Improving Drug Combination Repositioning using Positive Unlabelled Learning and Ensemble Learning

Drug repositioning is a cost-effective and time-effective concept that enables the use of existing drugs/drug combinations for therapeutic effects. The number of drug combinations used for therapeutic effects is smaller than all possible drug combinations in the present drug databases. These databases consist of a smaller set of labelled positives and a majority of unlabelled drug combinations. Therefore, there is a need for determining both reliable positive and reliable negative samples to develop binary classification models. Since, we only have labelled positives, the unlabelled data has to be separated into positives and negatives by a reliable technique. This study proposes and demonstrates the significance of using Positive Unlabelled Learning, for determining reliable positive and negative drug combinations for drug repositioning. In the proposed approach, the dataset with known positives and unlabelled samples was clustered by a Deep Learning based Self Organizing Map. Then, an ensemble learning methodology was followed by employing three classification models. The proposed PUL model was compared with the frequently used approach that randomly selects negative drug pairs from unlabelled samples. A significant improvement of 19.15%, 20.56% and 20.23% in the Precision, Recall and F-measure, respectively, was observed for the proposed PUL-based ensemble learning approach. Moreover, 128 drug repositioning candidates were predicted by the proposed methodology. Further, we found literature-based evidence to support five drug combinations that may be able to be repositioned. These discoveries show our proposed PUL approach as a promising strategy that is applicable in drug combination prediction for repositioning.

285 (PB065) - Using Fluorescence Cross Correlation Spectroscopy to Monitor and Quantify Drug Target Occupancy in Cells, Tissues and Patient Biopsies

Fluorescence Cross Correlation Spectroscopy (FCCS) is a singly molecule sensitive method to analyze molecular interactions. The approach has been employed in the past by Intana to set up high throughput screens, based on affinity or enzymatic activities. Similarly, we have investigated the formation and assembly of large protein complexes and studied complex targets as channels, transporters and even GPCRs. Among many other advantages, FCCS allows measurements in cellular lysates, bypassing protein purification and retaining the physiological environment. Consequently, assay development is straight forward and allows tackling also demanding targets. With this study we aimed to quantify the absolut drug target occupancy in cell in order to correlate the ratio of inhibited target molecules to the physiological, i.e. therapeutic effect. In order to measure unlabeled drugs and targets without purification of either binding partner for subsequent analysis Intana has set up an FCCS based assay format making use of fluorescent antibodies to label targets without interfering with drug target interactions and fluorescent tracer to quantify the unoccupied fraction of targets. For this samples are processed to cellular lysates using proprietary protocols and co-incubated with 2 different antibodies, carrying 2 different fluorescent labels and binding independently on 2 epitopes on the target. By this the total concentration of targets in the lysate can be precisely quantified. In a second step a fluorescent tracer molecule, which binds into the same pocket as the drug of interest is added to the lysate to quantify all unoccupied targets. In addition, a fluorescent antibody directed against the target is added for increase sensitivity. Knowing the exact concentration of targets in the sample and the concentration of targets that is bound to the tracer yields a precise quantification of target occupancy in the sample. Our approach was tested in cells, tissues, xenograft tumors and clinical patient samples. Data can help to guide cellular and animal experiments and to correlate or stratify patient responses to drug treatments. Conflict of interest: Ownership: Intana Bioscince GmbH, siTOOLs Biotech GmbH, Alpine Antiviral GmbH.

MolRoPE-BERT: An enhanced molecular representation with Rotary Position Embedding for molecular property prediction

Molecular property prediction is a significant task in drug discovery. Most deep learning-based computational methods either develop unique chemical representation or combine complex model. However, researchers are less concerned with the possible advantages of enormous quantities of unlabeled molecular data. Since the obvious limited amount of labeled data available, this task becomes more difficult. In some senses, SMILES of the drug molecule may be regarded of as a language for chemistry, taking inspiration from natural language processing research and current advances in pretrained models. In this paper, we incorporated Rotary Position Embedding(RoPE) efficiently encode the position information of SMILES sequences, ultimately enhancing the capability of the BERT pretrained model to extract potential molecular substructure information for molecular property prediction. We proposed the MolRoPE-BERT framework, an new end-to-end deep learning framework that integrates an efficient position coding approach for capturing sequence position information with a pretrained BERT model for molecular property prediction. To generate useful molecular substructure embeddings, we first exclusively train the MolRoPE-BERT on four million unlabeled drug SMILES(i.e., ZINC 15 and ChEMBL 27). Then, we conduct a series of experiments to evaluate the performance of our proposed MolRoPE-BERT on four well-studied datasets. Compared with conventional and state-of-the-art baselines, our experiment demonstrated comparable or superior performance.

Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction.

Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected pharmacological effects. Therefore, efficient identification of drug interactions is essential for the treatment of complex diseases. Currently proposed calculation methods are often limited by the collection of redundant drug features, a small amount of labeled data and low model generalization capabilities. Meanwhile, there is also a lack of unique methods for multidrug representation learning, which makes it more difficult to take full advantage of the originally scarce data. Inspired by graph models and pretraining models, we integrated a large amount of unlabeled drug molecular graph information and target information, then designed a pretraining framework, MGP-DR (Molecular Graph Pretraining for Drug Representation), specifically for drug pair representation learning. The model uses self-supervised learning strategies to mine the contextual information within and between drug molecules to predict drug-drug interactions and drug combinations. The results achieved promising performance across multiple metrics compared with other state-of-the-art methods. Our MGP-DR model can be used to provide a reliable candidate set for the combined use of multiple drugs. Code of the model, datasets and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MGP-DR). Supplementary data are available at Bioinformatics online.

Abstract 5076: Evaluation of the CNS penetration of a next generation PARP inhibitor, AZD9574, in cynomolgus monkey using positron emission tomography

Abstract The current clinically approved PARP inhibitors have limited subtype selectivity and are to some degree restricted in their ability to penetrate the central nervous system (CNS) due to efflux transporters, potentially limiting their efficacy in treating metastatic disease or primary tumors in the brain. The current study evaluated the potential of AZD9574, a next generation, PARP1 selective inhibitor/trapper, to penetrate the CNS in cynomolgus monkey, and its occupancy of the PARP1 enzyme, using positron emission tomography (PET). In vitro bidirectional efflux assay data suggested AZD9574 showed minimal substrate potential compared to the clinically approved PARP inhibitors. This was reflected in an increased ratio of unbound brain to unbound plasma concentration (Kpuu) in the rat of ~0.31. Therefore AZD9574 was taken forward into cynomolgus monkey PET studies. Firstly, the Kpuu was determined following dosing of [11C]AZD9574, co-administered with unlabeled drug to minimize the impact of specific binding. The high specific signal observed lead to the development of [11C]AZ3391, a PARP1 selective, CNS penetrant PET tracer, which was subsequently used to directly assess the PARP1 target engagement of AZD9574 in the brain. AZD9574 was found to show a Kpuu in cynomolgus monkeys of 0.79, close to unity with unbound plasma concentrations suggesting minimal CNS restriction. Furthermore, an i.v. infusion dose response study with AZD9574, conducted to examine its ability to block target occupancy by the PET tracer [11C]AZ3391, demonstrated a reduction in [11C]AZ3391 accumulation in whole brain. The resulting calculated occupancy of AZD9574 ranged from 17% for the lowest dose (0.003 mg/kg) to 95% for the highest dose tested (0.05 mg/kg). Comparable reduction in occupancy was seen for peripheral tissue, such as bone marrow, supporting the conclusion that AZD9574 shows minimal CNS restriction. These data show that AZD9574 is the first PARP inhibitor to reach the clinic which combines PARP1 selectivity, trapping and high CNS penetration in a single molecule and supports its development as a potential therapy for the treatment of metastatic disease and primary brain tumors. Citation Format: Andy Pike, Amber Balazs, Zsolt Cselényi, Sébastien L. Degorce, Avipsa Ghosh, Sudhir M. Hande, Jeffrey Johannes, Peter Johnström, Martin J. Packer, Magnus Schou, XiaoLan Zheng. Evaluation of the CNS penetration of a next generation PARP inhibitor, AZD9574, in cynomolgus monkey using positron emission tomography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5076.

Overcoming differential tumor penetration of BRAF inhibitors using computationally guided combination therapy.

BRAF-targeted kinase inhibitors (KIs) are used to treat malignancies including BRAF-mutant non–small cell lung cancer, colorectal cancer, anaplastic thyroid cancer, and, most prominently, melanoma. However, KI selection criteria in patients remain unclear, as are pharmacokinetic/pharmacodynamic (PK/PD) mechanisms that may limit context-dependent efficacy and differentiate related drugs. To address this issue, we imaged mouse models of BRAF-mutant cancers, fluorescent KI tracers, and unlabeled drug to calibrate in silico spatial PK/PD models. Results indicated that drug lipophilicity, plasma clearance, faster target dissociation, and, in particular, high albumin binding could limit dabrafenib action in visceral metastases compared to other KIs. This correlated with retrospective clinical observations. Computational modeling identified a timed strategy for combining dabrafenib and encorafenib to better sustain BRAF inhibition, which showed enhanced efficacy in mice. This study thus offers principles of spatial drug action that may help guide drug development, KI selection, and combination.

NNAN: Nearest Neighbor Attention Network to Predict Drug-Microbe Associations.

Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug–microbe associations (DMAs) before the drug administrations. Nevertheless, traditional DMA determination cannot be applied in a wide range due to the tremendous number of microbe species, high costs, and the fact that it is time-consuming. Thus, predicting possible DMAs in computer technology is an essential topic. Inspired by other issues addressed by deep learning, we designed a deep learning-based model named Nearest Neighbor Attention Network (NNAN). The proposed model consists of four components, namely, a similarity network constructor, a nearest-neighbor aggregator, a feature attention block, and a predictor. In brief, the similarity block contains a microbe similarity network and a drug similarity network. The nearest-neighbor aggregator generates the embedding representations of drug–microbe pairs by integrating drug neighbors and microbe neighbors of each drug–microbe pair in the network. The feature attention block evaluates the importance of each dimension of drug–microbe pair embedding by a set of ordinary multi-layer neural networks. The predictor is an ordinary fully-connected deep neural network that functions as a binary classifier to distinguish potential DMAs among unlabeled drug–microbe pairs. Several experiments on two benchmark databases are performed to evaluate the performance of NNAN. First, the comparison with state-of-the-art baseline approaches demonstrates the superiority of NNAN under cross-validation in terms of predicting performance. Moreover, the interpretability inspection reveals that a drug tends to associate with a microbe if it finds its top-l most similar neighbors that associate with the microbe.

Mol‐BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction

Molecular property prediction is an essential task in drug discovery. Most computational approaches with deep learning techniques either focus on designing novel molecular representation or combining with some advanced models together. However, researchers pay fewer attention to the potential benefits in massive unlabeled molecular data (e.g., ZINC). This task becomes increasingly challenging owing to the limitation of the scale of labeled data. Motivated by the recent advancements of pretrained models in natural language processing, the drug molecule can be naturally viewed as language to some extent. In this paper, we investigate how to develop the pretrained model BERT to extract useful molecular substructure information for molecular property prediction. We present a novel end‐to‐end deep learning framework, named Mol‐BERT, that combines an effective molecular representation with pretrained BERT model tailored for molecular property prediction. Specifically, a large‐scale prediction BERT model is pretrained to generate the embedding of molecular substructures, by using four million unlabeled drug SMILES (i.e., ZINC 15 and ChEMBL 27). Then, the pretrained BERT model can be fine‐tuned on various molecular property prediction tasks. To examine the performance of our proposed Mol‐BERT, we conduct several experiments on 4 widely used molecular datasets. In comparison to the traditional and state‐of‐the‐art baselines, the results illustrate that our proposed Mol‐BERT can outperform the current sequence‐based methods and achieve at least 2% improvement on ROC‐AUC score on Tox21, SIDER, and ClinTox dataset.

Elevated Vacuolar Uptake of Fluorescently Labeled Antifungal Drug Caspofungin Predicts Echinocandin Resistance in Pathogenic Yeast.

Echinocandins are the newest class of antifungal drugs in clinical use. These agents inhibit β-glucan synthase, which catalyzes the synthesis of β-glucan, an essential component of the fungal cell wall, and have a high clinical efficacy and low toxicity. Echinocandin resistance is largely due to mutations in the gene encoding β-glucan synthase, but the mode of action is not fully understood. We developed fluorescent probes based on caspofungin, the first clinically approved echinocandin, and studied their cellular biology in Candida species, the most common cause of human fungal infections worldwide. Fluorescently labeled caspofungin probes, like the unlabeled drug, were most effective against metabolically active cells. The probes rapidly accumulated in Candida vacuoles, as shown by colocalization with vacuolar proteins and vacuole-specific stains. The uptake of fluorescent caspofungin is facilitated by endocytosis: The labeled drug formed vesicles similar to fluorescently labeled endocytic vesicles, the vacuolar accumulation of fluorescent caspofungin was energy-dependent, and inhibitors of endocytosis reduced its uptake. In a panel comprised of isogenic Candida strains carrying different β-glucan synthase mutations as well as clinical isolates, resistance correlated with increased fluorescent drug uptake into vacuoles. Fluorescent drug uptake also associated with elevated levels of chitin, a sugar polymer that increases cell-wall rigidity. Monitoring the intracellular uptake of fluorescent caspofungin provides a rapid and simple assay that can enable the prediction of echinocandin resistance, which is useful for research applications as well as for selecting the appropriate drugs for treatments of invasive fungal infections.

Confirmatory Cut Point has Limited Ability to Make Accurate Classifications in Immunogenicity Assays

Aim: Competitive inhibition with excess unlabeled drug is used to confirm the presence of antidrug antibodies (ADA) in study samples. We evaluated specific and nonspecific responses from both drug-naive and drug-treated subjects to identify conditions required by the confirmatory assay to make accurate ADA classifications. Results: Nonspecific signal measured in drug-naive samples used to determine assay cut points was uniformly low and close to the screening cut point. Confirmatory assays performed on incurred study samples with nonspecific responses significantly above the level observed during cut point determination resulted in incorrect ADA classifications. Conclusion: Intensity of confirmatory response should be proportional to the screening response and therefore, to ensure accurate ADA classifications, the confirmatory responses cannot be considered as independent but need to be evaluated in relation to the screening responses.

Intensive monitoring of adverse drug events associated with the use of new glucose‐lowering drugs: results from an inception cohort study in Portugal

To determine the frequency and the time-course profile of adverse drug events associated with new glucose-lowering drugs in daily practice and to explore factors potentially associated to these events. An inception cohort study was implemented. Adults with type 2 diabetes mellitus initiating a dipeptidyl peptidase-4 inhibitor, a glucagon-like peptide-1 receptor agonist or a sodium-glucose co-transporter-2 inhibitor were eligible for inclusion. Data were collected through baseline and follow-up telephone questionnaires, administered at 2 weeks, 3 months and 6 months. Kaplan-Meier curves and log-rank were computed to compare the time to adverse drug event onset. Cox models were used to explore potential factors associated with adverse drug events. A total of 1328 participants were recruited to the study. In all, 1118 adverse drug events were reported (of which 36% were not listed in the summary of product characteristics) by 41% of participants. The median latency time of adverse drug events reported in ≥1% of participants ranged from 0 to 2 days. Glucagon-like peptide-1 receptor agonist and sodium-glucose co-transporter-2 inhibitor subgroups were associated with an increased likelihood of adverse drug event reporting when compared with the dipeptidyl peptidase-4 inhibitor subgroup. A total of 328 glucose-lowering drugs were withdrawn, more than half as a result of an adverse drug event. More than two-fifths of participants reported an adverse drug event; dipeptidyl peptidase-4 inhibitors led to the highest proportion of unlabelled adverse drug events. Adverse drug event latency time data show that counselling and adverse drug event management should be proactively addressed from treatment initiation. There should be greater focus on prevalent new users of glucose-lowering drugs, who were more complex participants in this study in terms of type 2 diabetes disease, as they were more likely to report an adverse drug event than the incident new users.

A convenient strategy to overcome interference in LC-MS/MS analysis: Application in a microdose absolute bioavailability study

Stable isotope labeled (SIL) compounds have been commonly used as internal standards (IS) to ensure the accuracy and quality of liquid chromatography–mass spectrometry (LC–MS) bioanalytical assays. Recently, the application of SIL drugs and LC–MS assays to microdose absolute bioavailability (BA) studies has gained increasing attention. This approach can provide significant cost and time saving, and higher data quality compared to the accelerator mass spectrometry (AMS)-based method, since it avoids the use of radioactive drug, high-cost AMS instrumentation and complex measurement processes. It also eliminates potential metabolite interference with AMS-based assay. However, one major challenge in the application of this approach is the potential interference between the unlabeled drug, the microdose SIL drug, and the SIL-IS during LC–MS analysis. Here we report a convenient and cost-effective strategy to overcome the interference by monitoring the isotopic ion (instead of the commonly used monoisotopic ion) of the interfered compound in MS analysis. For the BMS-986205 absolute BA case study presented, significant interference was observed from the microdose IV drug [13C7,15N]-BMS-986205 to its SIL-IS, [13C7,15N, D3]-BMS-986205, since the difference of nominal molecular mass between the two compounds is only 3 mu, and there is a Cl atom in the molecules. By applying this strategy (monitoring the 37Cl ion for the analysis of the IS), a 90-fold reduction of interference was achieved, which allowed the use of a synthetically accessible SIL compound and enabled the fast progress of the absolute BA study. This strategy minimizes the number of stable isotope labels used for avoiding interference, which greatly reduces the difficulty in synthesizing the SIL compounds and generates significant time and cost savings. In addition, this strategy can also be used to reduce the MS response of the analyte, therefore, avoiding the detector saturation issue of LC–MS/MS assay for high concentration BMS-986205. A LC–MS/MS assay utilizing this strategy was successfully developed for the simultaneous analysis of BMS-986205 and [13C7, 15N]-BMS-986205 in dog plasma using [13C7,15N, D3]-BMS-986205 as the IS. The assay was successfully applied to a microdose absolute BA study of BMS-986205 in dogs. The assay was also validated in human plasma and used to support a human absolute BA study. The same strategy can also be applied to other compounds, including those not containing Cl or other elements with abundant isotopes, or other applications (e.g. selection of internal standard), and the applications were presented.

Cell-Nanoparticle Interactions at (Sub)-Nanometer Resolution Analyzed by Electron Microscopy and Correlative Coherent Anti-Stokes Raman Scattering.

A wide variety of nanoparticles are playing an increasingly important role in drug delivery. Label-free imaging techniques are especially desirable to follow the cellular uptake and intracellular fate of nanoparticles. The combined correlative use of different techniques, each with unique advantages, facilitates more detailed investigation about such interactions. The synergistic use of correlative coherent anti-Stokes Raman scattering and electron microscopy (C-CARS-EM) imaging offers label-free, chemically-specific, and (sub)-nanometer spatial resolution for studying nanoparticle uptake into cells as demonstrated in the current study. Coherent anti-Stokes Raman scattering (CARS) microscopy offers chemically-specific (sub)micron spatial resolution imaging without fluorescent labels while transmission electron microscopy (TEM) offers (sub)-nanometer scale spatial resolution and thus visualization of precise nanoparticle localization at the sub-cellular level. This proof-of-concept imaging platform with unlabeled drug nanocrystals and macrophage cells revealed good colocalization between the CARS signal and electron dense nanocrystals in TEM images. The correlative TEM images revealed subcellular localization of nanocrystals inside membrane bound vesicles, showing multivesicular body (MVB)-like morphology typical for late endosomes (LEs), endolysosomes, and phagolysosomes. C-CARS-EM imaging has much potential to study the interactions between a wide range of nanoparticles and cells with high precision and confidence.

Improved Label Propagation Model to Predict Drug - drug Interactions

Drug-drug interactions (DDIs) is one of the most concerned issues in drug design. Accurate prediction of potential DDIs in clinical trials can reduce the occurrence of side effects in real life of drugs. Therefore, we propose a model to predict DDIs. The model integrates several methods that can improve label propagation algorithm. Firstly, the chi-square test (CHI) method is adopted to filter or select the features that contain a large amount of information. Secondly, the sample similarity calculation method is reconstructed by label similarity and feature similarity. Then the label initialization information of unlabeled samples is constructed. Finally, we use label propagation algorithm to estimate the labels of the unlabeled drugs. The results show that the proposed model can obtain higher the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPR), which provides a favorable guarantee for the discovery of DDIs in the clinical stage.

Mapping of drug distribution in the rabbit liver tumor model by complementary fluorescence and mass spectrometry imaging

This study describes the use of fluorescence imaging and mass spectrometry imaging, for imaging the anti-angiogenic drug sunitinib, used to treat liver cancer. These techniques allowed for the assessment of local delivery of the unlabeled therapeutic drug. More specifically, the spatial distribution of the drug and its metabolites after local administration was investigated, and drug levels in tumor and liver tissue over time were quantified.For this purpose, sunitinib-eluting microspheres were locoregionally injected into the tumor feeding arteries of rabbits bearing liver tumors. In adjacent areas of tumor and non-targeted contralateral liver tissue, sunitinib distribution was mapped around beads in occluded vessels 7, 12, 13 and 14days after embolization by means of the two imaging methods. Presence of sunitinib metabolites was assessed by mass spectrometry imaging.Sunitinib was found around microspheres in the tumor at day 7, 12, and 13. The drug was retained by the necrotic tumor tissue, resulting in homogeneously distributed and high levels of up to 40μg/g tissue in a 1.5mm radius around the beads. The drug was almost completely eliminated from the contralateral liver tissue. Several of the drug's metabolites, including its primary active metabolite SU12662, were detected in the tumor tissue over 13days.Sunitinib diffused from the beads and was retained at high, therapeutic levels during 13days. This was confirmed independently by complementary fluorescence and mass spectrometry imaging, which served as tools to confirm effective drug delivery after hepatic transarterial administration in situ.Compound: Sunitinib: PubChem CID 5329102.

Evaluation of the Lysophosphatidic Acid Receptor Type 1 Radioligand 11C-BMT-136088 for Lung Imaging in Rhesus Monkeys.

The lysophosphatidic acid receptor type 1 (LPA1) is 1 of 6 known receptors of the extracellular signaling molecule lysophosphatidic acid. It mediates effects such as cell proliferation, migration, and differentiation. In the lung, LPA1 is involved in pathways leading, after lung tissue injury, to pulmonary fibrosis instead of normal healing, by mediating fibroblast recruitment and vascular leakage. Thus, a LPA1 PET radiotracer may be useful for studying lung fibrosis or for developing LPA1-targeting drugs. We developed and evaluated the radiotracer 11C-BMT-136088 (1-(4'-(3-methyl-4-(((1(R)-(3-11C-methylphenyl)ethoxy)carbonyl)amino)isoxazol-5-yl)-[1,1'-biphenyl]-4-yl)cyclopropane-1-carboxylic acid) in rhesus monkeys to image LPA1 in the lung in vivo with PET. Methods: The study consisted of 3 parts: test-retest scans; self-saturation to estimate the tracer's in vivo dissociation constant, nondisplaceable volume of distribution (VND), and nondisplaceable binding potential (BPND); and dosimetry. In the first 2 parts, the radiotracer was administered using a bolus-plus-infusion protocol, the arterial input function was measured, and the animals underwent 2 scans per day separated by about 4 h. Lung regions of interest were segmented, and the tissue density estimated, from CT images. A fixed blood volume correction was applied. The tracer volume of distribution (VT) was estimated using multilinear analysis 1 (MA1) or equilibrium analysis (EA). Results:11C-BMT-136088 baseline VT was 1.83 ± 0.16 (MA1, n = 5) or 2.1 ± 0.55 (EA, n = 7) mL of plasma per gram of tissue in the left and right lung regions of interest, with a test-retest variability of -6% (MA1, n = 1) or -1% ± 14% (EA, n = 2). For the self-saturation study, 11C-BMT-136088 VND and BPND were estimated to be 0.9 ± 0.08 mL of plasma per gram of tissue and 1.1 ± 0.14, respectively. The unlabeled drug dose and plasma concentration leading to a 50% reduction of 11C-BMT-136088 specific binding were 73 ± 30 nmol/kg and 28 ± 12 nM, respectively. The average plasma free fraction was 0.2%; thus, the tracer's in vivo dissociation constant was estimated to be 55 pM. For the dosimetry study, the highest organ dose was in the liver (43.1 ± 4.9 and 68.9 ± 9.4 μSv/MBq in reference human male and female phantoms, respectively), and the effective dose equivalent was 6.9 ± 0.6 and 8.7 ± 0.6 μSv/MBq, respectively. Conclusion: Specific binding of 11C-BMT-136088 can be reliably measured to quantify LPA1 in the lungs of rhesus monkeys in vivo.

Bridging immunogenicity assays for IgG4 therapeutics: mitigating interference from Fc-Fc interactions.

A bridging immunogenicity assay for a human IgG4 mAb therapeutic was transferred to an automation system to increase throughput. However, background signal increased five- to six-fold during the 6- to 8-h run. Noncovalent Fc contacts formed between labeled IgG4 drugs in reagent solutions stored during the automation run. This generated substantial background signal, reducing assay sensitivity by approximately sixfold. Fc interactions also significantly impacted the confirmation assay. Fc contacts formed between labeled and unlabeled drug, significantly increasing signal inhibition (∼7-70%) in the 6-h run. Storing labeled antibody solutions separately and combining them immediately before adding to samples reduced interference from Fc interactions. Preincubation time for reagent solutions should be strictly controlled for anti-drug antibody assays with IgG4 drugs to avoid false-positive results.

Estimating preferences for modes of drug administration: The case of US healthcare professionals.

There are hidden drug administration costs that arise from a mismatch between end-user preferences and how manufacturers choose to formulate their drug products for delivery to patients. The corollary of this is: there are "intangible benefits" from considering end-user preferences in manufacturing patient-friendly medicines. It is important then to have some idea of what pharmaceutical manufacturers should consider in making patient-friendly medicines and of the magnitude of the indirect benefits from doing so. This study aimed to evaluate preferences of healthcare professionals in the US for the non-monetary attributes of different modes of drug administration. It uses these preference orderings to compute a monetary valuation of the indirect benefits from making patient-friendly medicines. A survey collected choice preferences of a sample of 210 healthcare professionals in the US for two unlabelled drug options. These drugs were identical except in the levels of attributes of drug administration. Using the choice data collected, statistical models were estimated to compute gross welfare benefits, measured by the expected compensating variation, from making drugs in a more patient-friendly manner. The monetary value of end-user benefits from developing patient-friendly drug delivery systems is: (1) as large as the annual acquisition costs per full treatment episode for some biologic drugs; and (2) likely to fall in the "high end" of the distribution of the direct monetary costs of drug administration. An examination of end-user preferences should help manufacturers make more effective and efficient use of limited resources for innovations in drug delivery system, or manufacturing research in general.

Limited Tumor Tissue Drug Penetration Contributes to Primary Resistance against Angiogenesis Inhibitors.

Resistance mechanisms against antiangiogenic drugs are unclear. Here, we correlated the antitumor and antivascular properties of five different antiangiogenic receptor tyrosine kinase inhibitors (RTKIs) (motesanib, pazopanib, sorafenib, sunitinib, vatalanib) with their intratumoral distribution data obtained by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI). In the first mouse model, only sunitinib exhibited broad-spectrum antivascular and antitumor activities by simultaneously suppressing vascular endothelial growth factor receptor-2 (VEGFR2) and desmin expression, and by increasing intratumoral hypoxia and inhibiting both tumor growth and vascularisation significantly. Importantly, the highest and most homogeneous intratumoral drug concentrations have been found in sunitinib-treated animals. In another animal model, where - in contrast to the first model - vatalanib was detectable at homogeneously high intratumoral concentrations, the drug significantly reduced tumor growth and angiogenesis. In conclusion, the tumor tissue penetration and thus the antiangiogenic and antitumor potential of antiangiogenic RTKIs vary among the tumor models and our study demonstrates the potential of MALDI-MSI to predict the efficacy of unlabelled small molecule antiangiogenic drugs in malignant tissue. Our approach is thus a major technical and preclinical advance demonstrating that primary resistance to angiogenesis inhibitors involves limited tumor tissue drug penetration. We also conclude that MALDI-MSI may significantly contribute to the improvement of antivascular cancer therapies.