Application of a Biomimetic Nanoparticle-Based Mock Virus to Determine SARS-CoV-2 Neutralizing Antibody Levels in Blood Samples Using a Lateral Flow Assay.

The presence of neutralizing antibodies against SARS-CoV-2 in blood, acquired through previous infection or vaccination, is known to prevent the (re)occurrence of outbreaks unless the virus mutates. Therefore, the measurement of neutralizing antibodies constitutes an indispensable tool in assessing an individual's and a population's immunity against SARS-CoV-2. For this reason, we have developed an innovative lateral flow assay (LFA) capable of detecting blood-derived neutralizing antibodies using a biomimetic SARS-CoV-2 mock virus system. Here, functionalized gold nanoparticles (AuNPs) featuring the trimeric spike (S) protein at its surface imitate the virus's structure and are applied to monitor the presence and efficacy of neutralizing antibodies in blood samples. The detection principle relies on the interaction between mock virus and the immobilized angiotensin-converting enzyme 2 (ACE2) receptor, which is inhibited when neutralizing antibodies are present. To further enhance the sensitivity of our competitive assay and identify low titers of neutralizing antibodies, an additional mixing pad is embedded into the device to increase the interaction time between mock virus and neutralizing antibodies. The developed LFA is benchmarked against the WHO International Standard (21/338) and demonstrated reliable quantification of neutralizing antibodies that inhibit ACE2 binding events down to a detection limit of an antibody titer of 59 IU/mL. Additional validation using whole blood and plasma samples showed reproducible results and good comparability to a laboratory-based reference test, thus highlighting its applicability for point-of-care testing.

Reagent storage and delivery on integrated microfluidic chips for point-of-care diagnostics.

Microfluidic-based point-of-care diagnostics offer several unique advantages over existing bioanalytical solutions, such as automation, miniaturisation, and integration of sensors to rapidly detect on-site specific biomarkers. It is important to highlight that a microfluidic POC system needs to perform a number of steps, including sample preparation, nucleic acid extraction, amplification, and detection. Each of these stages involves mixing and elution to go from sample to result. To address these complex sample preparation procedures, a vast number of different approaches have been developed to solve the problem of reagent storage and delivery. However, to date, no universal method has been proposed that can be applied as a working solution for all cases. Herein, both current self-contained (stored within the chip) and off-chip (stored in a separate device and brought together at the point of use) are reviewed, and their merits and limitations are discussed. This review focuses on reagent storage devices that could be integrated with microfluidic devices, discussing further issues or merits of these storage solutions in two different sections: direct on-chip storage and external storage with their application devices. Furthermore, the different microvalves and micropumps are considered to provide guidelines for designing appropriate integrated microfluidic point-of-care devices.

Database of 4 Million Medicinal Chemistry-Relevant Ring Systems.

Central ring systems are the most important part of bioactive molecules. They determine molecule shape, keep substituents in their proper positions, and also influence global molecular properties. In the present study, a database of 4 million medicinal chemistry-relevant ring systems has been created, not by crude random enumeration but by applying a set of rules derived by analyzing rings present in bioactive molecules. The aromatic properties and tautomer stability of generated rings have also been considered to ensure that the rings in the database are stable and chemically reasonable. 99.2% of these rings are novel and not included in molecules in the ChEMBL or PubChem databases. This large database of ring systems has been created with the goal to provide support for bioisosteric design and scaffold hopping as well as to be used in generative chemistry applications. The complete set of created rings is available for download in the SMILES format from https://peter-ertl.com/molecular/data/.

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security. The experiments involved an unprecedented cross-pharma data set of 2.6+ billion confidential experimental activity data points, documenting 21+ million physical small molecules and 40+ thousand assays in on-target and secondary pharmacodynamics and pharmacokinetics. Appropriate complementary metrics were developed to evaluate the predictive performance in the federated setting. In addition to predictive performance increases in labeled space, the results point toward an extended applicability domain in federated learning. Increases in collective training data volume, including by means of auxiliary data resulting from single concentration high-throughput and imaging assays, continued to boost predictive performance, albeit with a saturating return. Markedly higher improvements were observed for the pharmacokinetics and safety panel assay-based task subsets.

Design and Biochemical Characterization of Peptidic Inhibitors of the Myb/p300 Interaction.

The Myb transcription factor is involved in the proliferation of hematopoietic cells, and deregulation of its expression can lead to cancers such as leukemia. Myb interacts with various proteins, including the histone acetyltransferases p300 and CBP. Myb binds to a small domain of p300, the KIX domain (p300KIX), and inhibiting this interaction is a potential new drug discovery strategy in oncology. The available structures show that Myb binds to a very shallow pocket of the KIX domain, indicating that it might be challenging to identify inhibitors of this interaction. Here, we report the design of Myb-derived peptides which interact with p300KIX. We show that by mutating only two Myb residues that bind in or near a hotspot at the surface of p300KIX, it is possible to obtain single-digit nanomolar peptidic inhibitors of the Myb/p300KIX interaction that bind 400-fold tighter to p300KIX than wildtype Myb. These findings suggest that it might also be possible to design potent low molecular-weight compounds to disrupt the Myb/p300KIX interaction.

The most common linkers in bioactive molecules and their bioisosteric replacement network.

Structures of the large majority of bioactive molecules are composed of several rings that are decorated by substituents and connected by linkers. While numerous cheminformatics studies focusing on rings and substituents are available, practically nothing has been published about the third important structural constituent of bioactive molecules - the linkers. The current study attempts to fill this gap. The most common linkers present in bioactive molecules are identified, their properties analyzed and a method for linker similarity search introduced. The bioisosteric replacement network of linkers is generated based on a large corpus of structure-activity data from medicinal chemistry literature. The results are presented in a graphical form and the underlying data are also made available for download. This analysis is intended to help medicinal chemists to better understand the role of linkers, particularly heterocyclic rings in bioactive molecules and to select an optimal set of linkers in their future project.

Which boronic acids are used most frequently for synthesis of bioactive molecules?

Boronic acids are essential building blocks used for the synthesis of bioactive molecules, the generation of chemical libraries and the exploration of structure-activity relationships. As a result, more than ten thousand boronic acids are commercially available. Medicinal chemists are therefore facing a challenge; which of them should they select to maximize information obtained by the synthesis of new target molecules. The present article aims to help them to make the right choices. The boronic acids used frequently in the synthesis of bioactive molecules were identified by mining several large molecular and reaction databases and their properties were analyzed. Based on the results a diverse set of boronic acids covering well the bioactive chemical space was selected and is suggested as a basis for library design for the efficient exploration of structure-activity relationships. A Boronic Acid Navigator web tool which helps chemists to make their own selection is also made available at https://bit.ly/boronics.

Towards a microfluidic H295R steroidogenesis assay-biocompatibility study and steroid detection on a thiol-ene-based chip.

The development of cell-based microfluidic assays offers exciting new opportunities in toxicity testing, allowing for integration of new functionalities, automation, and high throughput in comparison to traditional well-plate assays. As endocrine disruption caused by environmental chemicals and pharmaceuticals represents a growing global health burden, the purpose of the current study was to contribute towards the miniaturization of the H295R steroidogenesis assay, from the well-plate to the microfluidic format. Microfluidic chip fabrication with the established well-plate material polystyrene (PS) is expensive and complicated; PDMS and thiol-ene were therefore tested as potential chip materials for microfluidic H295R cell culture, and evaluated in terms of cell attachment, cell viability, and steroid synthesis in the absence and presence of collagen surface modification. Additionally, spike-recovery experiments were performed, to investigate potential steroid adsorption to chip materials. Cell aggregation with poor steroid recoveries was observed for PDMS, while cells formed monolayer cultures on the thiol-ene chip material, with cell viability and steroid synthesis comparable to cells grown on a PS surface. As thiol-ene overall displayed more favorable properties for H295R cell culture, a microfluidic chip design and corresponding cell seeding procedure were successfully developed, achieving repeatable and uniform cell distribution in microfluidic channels. Finally, H295R perfusion culture on thiol-ene chips was investigated at different flow rates (20, 10, and 2.5 µL/min), and 13 steroids were detected in eluting cell medium over 48 h at the lowest flow rate. The presented work and results pave the way for a time-resolved microfluidic H295R steroidogenesis assay.

How Organ-on-a-Chip Technology Can Assist in Studying the Role of the Glymphatic System in Neurodegenerative Diseases.

The lack of a conventional lymphatic system that permeates throughout the entire human brain has encouraged the identification and study of alternative clearance routes within the cerebrum. In 2012, the concept of the glymphatic system, a perivascular network that fluidically connects the cerebrospinal fluid to the lymphatic vessels within the meninges via the interstitium, emerged. Although its exact mode of action has not yet been fully characterized, the key underlying processes that govern solute transport and waste clearance have been identified. This review briefly describes the perivascular glial-dependent clearance system and elucidates its fundamental role in neurodegenerative diseases. The current knowledge of the glymphatic system is based almost exclusively on animal-based measurements, but these face certain limitations inherent to in vivo experiments. Recent advances in organ-on-a-chip technology are discussed to demonstrate the technology's ability to provide alternative human-based in vitro research models. Herein, the specific focus is on how current microfluidic-based in vitro models of the neurovascular system and neurodegenerative diseases might be employed to (i) gain a deeper understanding of the role and function of the glymphatic system and (ii) to identify new opportunities for pharmacological intervention.

The Cultivation Modality and Barrier Maturity Modulate the Toxicity of Industrial Zinc Oxide and Titanium Dioxide Nanoparticles on Nasal, Buccal, Bronchial, and Alveolar Mucosa Cell-Derived Barrier Models.

As common industrial by-products, airborne engineered nanomaterials are considered important environmental toxins to monitor due to their potential health risks to humans and animals. The main uptake routes of airborne nanoparticles are nasal and/or oral inhalation, which are known to enable the transfer of nanomaterials into the bloodstream resulting in the rapid distribution throughout the human body. Consequently, mucosal barriers present in the nose, buccal, and lung have been identified and intensively studied as the key tissue barrier to nanoparticle translocation. Despite decades of research, surprisingly little is known about the differences among various mucosa tissue types to tolerate nanoparticle exposures. One limitation in comparing nanotoxicological data sets can be linked to a lack of harmonization and standardization of cell-based assays, where (a) different cultivation conditions such as an air-liquid interface or submerged cultures, (b) varying barrier maturity, and (c) diverse media substitutes have been used. The current comparative nanotoxicological study, therefore, aims at analyzing the toxic effects of nanomaterials on four human mucosa barrier models including nasal (RPMI2650), buccal (TR146), alveolar (A549), and bronchial (Calu-3) mucosal cell lines to better understand the modulating effects of tissue maturity, cultivation conditions, and tissue type using standard transwell cultivations at liquid-liquid and air-liquid interfaces. Overall, cell size, confluency, tight junction localization, and cell viability as well as barrier formation using 50% and 100% confluency was monitored using trans-epithelial-electrical resistance (TEER) measurements and resazurin-based Presto Blue assays of immature (e.g., 5 days) and mature (e.g., 22 days) cultures in the presence and absence of corticosteroids such as hydrocortisone. Results of our study show that cellular viability in response to increasing nanoparticle exposure scenarios is highly compound and cell-type specific (TR146 6 ± 0.7% at 2 mM ZnO (ZnO) vs. ~90% at 2 mM TiO2 (TiO2) for 24 h; Calu3 93.9 ± 4.21% at 2 mM ZnO vs. ~100% at 2 mM TiO2). Nanoparticle-induced cytotoxic effects under air-liquid cultivation conditions declined in RPMI2650, A549, TR146, and Calu-3 cells (~0.7 to ~0.2-fold), with increasing 50 to 100% barrier maturity under the influence of ZnO (2 mM). Cell viability in early and late mucosa barriers where hardly influenced by TiO2 as well as most cell types did not fall below 77% viability when added to Individual ALI cultures. Fully maturated bronchial mucosal cell barrier models cultivated under ALI conditions showed less tolerance to acute ZnO nanoparticle exposures (~50% remaining viability at 2 mM ZnO for 24 h) than the similarly treated but more robust nasal (~74%), buccal (~73%), and alveolar (~82%) cell-based models.

Magic Rings: Navigation in the Ring Chemical Space Guided by the Bioactive Rings.

The large majority of bioactive molecules contain a more or less complex ring system as a central structural element. This central core determines the basic molecule shape, keeps substituents in their proper positions, and often also contributes to the biological activity itself. In this study the ring systems extracted from one billion molecules are processed and differences between rings from bioactive molecules and common synthetic molecules are analyzed. The bioactive rings seem to be distributed throughout the large portion of chemical space, but not uniformly; one can see several more dense regions, where the bioactive rings often appear in small clusters, as well as empty areas. A web tool offering an interactive navigation in the ring chemical space and supporting identification of bioisosteric ring analogs available at https://bit.ly/magicrings is also described.

Substituents of life: The most common substituent patterns present in natural products.

Comparison of substituents present in natural products with the substituents found in average synthetic molecules reveals considerable differences between these two groups. The natural products substituents contain mostly oxygen heteroatoms, are structurally more complex, often containing double bonds and are rich in stereocenters. Substituents found in synthetic molecules contain nitrogen and sulfur heteroatoms, halogenes and more aromatic and particularly heteroaromatic rings. The characteristics of substituents typical for natural products identified here can be useful in the medicinal chemistry context, for example to guide the synthesis of natural product-like libraries and natural product-inspired fragment collections. The results may be used also to support compound derivatization strategies and the design of pseudo-natural natural products.

A microfluidic microparticle-labeled impedance sensor array for enhancing immunoassay sensitivity.

An impedimetric biosensor is used to measure electrical impedance changes in the presence of biomolecules from sinusoidal input voltages. In this paper, we present a new portable impedance-based biosensor platform to improve the sensitivity of immunoassays with microparticles as a label. Using a 2 × 4 interdigitated electrode array with a 10/10 µm electrode/gap and a miniaturized impedance analyzer, we performed immunoassays with microparticles by integrating a microfluidic channel to evaluate signal enhancement. First, to understand the material dependency of microparticles on the sensor array, magnetic, silica, and polystyrene microparticles were tested. Among these microparticles, magnetic microparticles presented a high signal enhancement with relevant stability from the sensor array. With the magnetic microparticles, we demonstrate a series of immunoassays to detect human tumor necrosis factor (TNF-α) and compare the level of signal enhancement by measuring the limit of detection (LOD). With the microparticles, we achieved over ten times improvement of LOD from sandwich immunoassays. By incorporating with sample preparation and flow manipulation systems, this impedance sensor array can be utilized for digital diagnostics for a real sample-in answer-out system.

Monitoring the neurotransmitter release of human midbrain organoids using a redox cycling microsensor as a novel tool for personalized Parkinson's disease modelling and drug screening.

In this study, we have aimed at developing a novel electrochemical sensing approach capable of detecting dopamine, the main biomarker in Parkinson's disease, within the highly complex cell culture matrix of human midbrain organoids in a non-invasive and label-free manner. With its ability to generate organotypic structures in vitro, induced pluripotent stem cell technology has provided the basis for the development of advanced patient-derived disease models. These include models of the human midbrain, the affected region in the neurodegenerative disorder Parkinson's disease. Up to now, however, the analysis of so-called human midbrain organoids has relied on time-consuming and invasive strategies, incapable of monitoring organoid development. Using a redox-cycling approach in combination with a 3-mercaptopropionic acid self-assembled monolayer modification enabled the increase of sensor selectivity and sensitivity towards dopamine, while simultaneously reducing matrix-mediated interferences. In this work, we demonstrate the ability to detect and monitor even small differences in dopamine release between healthy and Parkinson`s disease-specific midbrain organoids over prolonged cultivation periods, which was additionally verified using liquid chromatography-multiple reaction monitoring mass spectrometry. Furthermore, the detection of a phenotypic rescue in midbrain organoids carrying a pathogenic mutation in leucine-rich repeat kinase 2, upon treatment with the leucine-rich repeat kinase 2 inhibitor II underlines the practical implementability of our sensing approach for drug screening applications as well as personalized disease modelling.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities.

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

Deliberations on Natural Products and Future Directions in the Pharmaceutical Industry.

Natural Products (NPs) are molecular' special equipment ' that impart survival benefits on their producers in nature. Due to their evolved functions to modulate biology these privileged metabolites are substantially represented in the drug market and are continuing to contribute to the discovery of innovative medicines such as the recently approved semi-synthetic derivative of the bacterial alkaloid staurosporin in oncology indications. The innovation of low molecular weight compounds in modern drug discovery is built on rapid progress in chemical, molecular biological, pharmacological and data sciences, which together provide a rich understanding of disease-driving molecular interactions and how to modulate them. NPs investigated in these pharmaceutical research areas create new perspectives on their chemical and biological features and thereby new chances to advance medical research. New methods in analytical chemistry linked with searchable NP-databases solved the issue of reisolation and enabled targeted and efficient access to novel molecules from nature. Cheminformatics delivers high resolution descriptions of NPs and explores the substructures that systematically map NP-chemical space by sp³-enriched fragments. Whole genome sequencing has revealed the existence of collocated gene clusters that form larger functional entities together with proximate resistance factors thus avoiding self-inhibition of the encoded metabolites. The analysis of bacterial and fungal genes provides tantalizing glimpses of new compound-target pairs of therapeutic value. Furthermore, a dedicated investigation of structurally unique, selectively active NPs in chemical biology demonstrates their extraordinary power as shuttles between new biological target spaces of pharmaceutical relevance.

Downscaling screening cultures in a multifunctional bioreactor array-on-a-chip for speeding up optimization of yeast-based lactic acid bioproduction.

A key challenge for bioprocess engineering is the identification of the optimum process conditions for the production of biochemical and biopharmaceutical compounds using prokaryotic as well as eukaryotic cell factories. Shake flasks and bench-scale bioreactor systems are still the golden standard in the early stage of bioprocess development, though they are known to be expensive, time-consuming, and labor-intensive as well as lacking the throughput for efficient production optimizations. To bridge the technological gap between bioprocess optimization and upscaling, we have developed a microfluidic bioreactor array to reduce time and costs, and to increase throughput compared with traditional lab-scale culture strategies. We present a multifunctional microfluidic device containing 12 individual bioreactors (Vt = 15 µl) in a 26 mm × 76 mm area with in-line biosensing of dissolved oxygen and biomass concentration. Following initial device characterization, the bioreactor lab-on-a-chip was used in a proof-of-principle study to identify the most productive cell line for lactic acid production out of two engineered yeast strains, evaluating whether it could reduce the time needed for collecting meaningful data compared with shake flasks cultures. Results of the study showed significant difference in the strains' productivity within 3 hr of operation exhibiting a 4- to 6-fold higher lactic acid production, thus pointing at the potential of microfluidic technology as effective screening tool for fast and parallelizable industrial bioprocess development.

Identification of Bioisosteric Substituents by a Deep Neural Network.

Bioisosteric design is a classical technique used in medicinal chemistry to improve potency, druglike properties, or the synthetic accessibility of a compound or to find similar potent compounds that exist in novel chemical space. Bioisosteric design involves replacing part of a molecule by another part that has similar properties. Such replacements may be identified by applying medicinal chemistry knowledge, by mining chemical databases or by choosing analogues similar in molecular physicochemical properties. In this article, a novel approach to identify bioisosteric analogues is described where the suggestions are made by a deep neural network trained on data collected from a large corpus of medicinal chemistry literature. The network trained in this way is able to mimic the decision making of experienced medicinal chemists and identify standard as well as nonclassical bioisosteric analogues, even for the structures outside the training set. Examples of the results are provided and application possibilities are discussed.

Natural product drug delivery: A special challenge?

Natural products have a long-standing and critical role in drug development and medical use. The structural and physicochemical properties of natural products, while derived evolutionarily to be effective in living systems, may create challenges in translation to a pharmaceutical product. Molecular complexity, low solubility, functional group reactivity and general instability are among the challenges that typically need to be overcome. This review looks at some of the ways that natural products have been formulated and delivered to enable the successful application of these vitally important medicines to patients.

A Systematic Cheminformatics Analysis of Functional Groups Occurring in Natural Products.

The two most striking features that discriminate natural products from synthetic molecules are their characteristic scaffolds and unique functional groups (FGs). In this study we systematically investigate the distribution of FGs in natural products from a cheminformatics perspective by comparing FG frequencies in natural products with those found in average synthetic molecules. We thereby aim for the identification of FGs that are characteristic for molecules produced by living organisms. In our analysis we also include information about the natural origins of the structures investigated, allowing us to link the occurrence of specific FGs to the individual producing species. Our findings have the potential for being applied in a medicinal chemistry context concerning the synthesis of natural product-like libraries and natural product-inspired fragment collections. The results may be used also to support compound derivatization strategies and the design of "non-natural" natural products.