ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.

Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.

Computational approaches streamlining drug discovery.

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

DrugRep: an automatic virtual screening server for drug repurposing.

Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/ .

Mycobacterium abscessus drug discovery using machine learning.

The prevalence of infections by nontuberculous mycobacteria is increasing, having surpassed tuberculosis in the United States and much of the developed world. Nontuberculous mycobacteria occur naturally in the environment and are a significant problem for patients with underlying lung diseases such as bronchiectasis, chronic obstructive pulmonary disease, and cystic fibrosis. Current treatment regimens are lengthy, complicated, toxic and they are often unsuccessful as seen by disease recurrence. Mycobacterium abscessus is one of the most commonly encountered organisms in nontuberculous mycobacteria disease and it is the most difficult to eradicate. There is currently no systematically proven regimen that is effective for treating M. abscessus infections. Our approach to drug discovery integrates machine learning, medicinal chemistry and in vitro testing and has been previously applied to Mycobacterium tuberculosis. We have now identified several novel 1-(phenylsulfonyl)-1H-benzimidazol-2-amines that have weak activity on M. abscessus in vitro but may represent a starting point for future further medicinal chemistry optimization. We also address limitations still to be overcome with the machine learning approach for M. abscessus.

Organs-on-chip: The way forward.

Organ-on-chip (OoC) technology is thriving thanks to stem cells availability and international OoC programs. Concerted standardization, qualification, and independent testing of devices are needed to coherently develop OoC technology further and fulfill its potential in drug development, disease modeling, and personalized medicine. The OoC roadmap can lead the way forward.

Validation of an adipose-liver human-on-a-chip model of NAFLD for preclinical therapeutic efficacy evaluation.

Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease and strongly correlates with the growing incidence of obesity and type II diabetes. We have developed a human-on-a-chip model composed of human hepatocytes and adipose tissue chambers capable of modeling the metabolic factors that contribute to liver disease development and progression, and evaluation of the therapeutic metformin. This model uses a serum-free, recirculating medium tailored to represent different human metabolic conditions over a 14-day period. The system validated the indirect influence of adipocyte physiology on hepatocytes that modeled important aspects of NAFLD progression, including insulin resistant biomarkers, differential adipokine signaling in different media and increased TNF-α-induced steatosis observed only in the two-tissue model. This model provides a simple but unique platform to evaluate aspects of an individual factor's contribution to NAFLD development and mechanisms as well as evaluate preclinical drug efficacy and reassess human dosing regimens.

Human iPSC-Based Modeling of Central Nerve System Disorders for Drug Discovery.

A high-throughput drug screen identifies potentially promising therapeutics for clinical trials. However, limitations that persist in current disease modeling with limited physiological relevancy of human patients skew drug responses, hamper translation of clinical efficacy, and contribute to high clinical attritions. The emergence of induced pluripotent stem cell (iPSC) technology revolutionizes the paradigm of drug discovery. In particular, iPSC-based three-dimensional (3D) tissue engineering that appears as a promising vehicle of in vitro disease modeling provides more sophisticated tissue architectures and micro-environmental cues than a traditional two-dimensional (2D) culture. Here we discuss 3D based organoids/spheroids that construct the advanced modeling with evolved structural complexity, which propels drug discovery by exhibiting more human specific and diverse pathologies that are not perceived in 2D or animal models. We will then focus on various central nerve system (CNS) disease modeling using human iPSCs, leading to uncovering disease pathogenesis that guides the development of therapeutic strategies. Finally, we will address new opportunities of iPSC-assisted drug discovery with multi-disciplinary approaches from bioengineering to Omics technology. Despite technological challenges, iPSC-derived cytoarchitectures through interactions of diverse cell types mimic patients' CNS and serve as a platform for therapeutic development and personalized precision medicine.

Imaging microphysiological systems: a review.

Microphysiological systems (MPS), often referred to as "organ-on-chips," are microfluidic-based in vitro models that aim to recapitulate the dynamic chemical and mechanical microenvironment of living organs. MPS promise to bridge the gap between in vitro and in vivo models and ultimately improve the translation from preclinical animal studies to clinical trials. However, despite the explosion of interest in this area in recent years, and the obvious rewards for such models that could improve R&D efficiency and reduce drug attrition in the clinic, the pharmaceutical industry has been slow to fully adopt this technology. The ability to extract robust, quantitative information from MPS at scale is a key requirement if these models are to impact drug discovery and the subsequent drug development process. Microscopy imaging remains a core technology that enables the capture of information at the single-cell level and with subcellular resolution. Furthermore, such imaging techniques can be automated, increasing throughput and enabling compound screening. In this review, we discuss a range of imaging techniques that have been applied to MPS of varying focus, such as organoids and organ-chip-type models. We outline the opportunities these technologies can bring in terms of understanding mechanistic biology, but also how they could be used in higher-throughput screens, widening the scope of their impact in drug discovery. We discuss the associated challenges of imaging these complex models and the steps required to enable full exploitation. Finally, we discuss the requirements for MPS, if they are to be applied at a scale necessary to support drug discovery projects.

Optimization of a Colorimetric Assay to Determine Lactate Dehydrogenase B Activity Using Design of Experiments.

Lactate dehydrogenase B (LDH-B) is overexpressed in lung and breast cancer, and it has been considered as a potential target to treat these types of cancer. Herein, we propose a straightforward incomplete factorial (IF) design composed of 12 combinations of two reaction buffers, three pH values, three salt (NaCl) concentrations, and three incubation times, which we called IF-BPST (Buffer/pH/Salt/Time), for the optimization of a colorimetric LDH-B assay in a final volume of 100 µL using 96-well plates. The assay is based on the absorbance change at ~570 nm and the color change of the reaction mixture due to the release of NADH that reacts with nitroblue tetrazolium (NBT) and phenazine methosulfate (PMS), resulting in the formation of a blue-purple formazan. The results obtained using the IF-BPST were comparable with those obtained by response surface methodology. Our work revealed that the NBT/PMS assay with some modifications can be used to measure the activity of LDH-B and other dehydrogenases in a high-throughput screening format at the early stages of drug discovery. LDH-B containing lysates cannot be assayed directly, however, due to the sensitivity of the method toward detergents. Thus, we suggest precipitating the proteins in the lysates to remove the interfering detergents, and then to dissolve the protein pellet in a suitable buffer and carry out the assay.

Future perspective: high-throughput construction of new ultrasensitive cytokine and virion liquid chips for high-throughput screening (HTS) of anti-inflammatory drugs or clinical diagnosis and treatment of inflammatory diseases.

Pathogen-host cell interactions play an important role in many human infectious and inflammatory diseases. Several pathogens, including Escherichia coli (E. coli), Mycobacterium tuberculosis (M. tb), and even the recent 2019 novel coronavirus (2019-nCoV), can cause serious breathing and brain disorders, tissue injury and inflammation, leading to high rates of mortality and resulting in great loss to human physical and mental health as well as the global economy. These infectious diseases exploit the microbial and host factors to induce serious inflammatory and immunological symptoms. Thus the development of anti-inflammatory drugs targeting bacterial/viral infection is an urgent need. In previous studies, YojI-IFNAR2, YojI-IL10RA, YojI-NRP1,YojI-SIGLEC7, and YojI-MC4R membrane-protein interactions were found to mediate E. coli invasion of the blood-brain barrier (BBB), which activated the downstream anti-inflammatory proteins NACHT, LRR and PYD domains-containing protein 2(NLRP2), using a proteomic chip conjugated with cell immunofluorescence labeling. However, the studies of pathogen (bacteria/virus)-host cell interactions mediated by membrane protein interactions did not extend their principles to broad biomedical applications such as 2019-nCoV infectious disease therapy. The first part of this feature article presents in-depth analysis of the cross-talk of cellular anti-inflammatory transduction signaling among interferon membrane protein receptor II (IFNAR2), interleukin-10 receptor subunit alpha (IL-10RA), NLRP2 and [Ca2+]-dependent phospholipase A2 (PLA2G5), based on experimental results and important published studies, which lays a theoretical foundation for the high-throughput construction of the cytokine and virion solution chip. The paper then moves on to the construction of the novel GPCR recombinant herpes virion chip and virion nano-oscillators for profiling membrane protein functions, which drove the idea of constructing the new recombinant virion and cytokine liquid chips for HTS of leading drugs. Due to the different structural properties of GPCR, IFNAR2, ACE2 and Spike of 2019-nCoV, their ligands will either bind the extracellular domain of IFNAR2/ACE2/Spike or the specific loops of the GPCR on the envelope of the recombinant herpes virions to induce dynamic charge distribution changes that lead to the variable electron transition for detection. Taken together, the combined overview of two of the most innovative and exciting developments in the immunoinflammatory field provides new insight into high-throughput construction of ultrasensitive cytokine and virion liquid chips for HTS of anti-inflammatory drugs or clinical diagnosis and treatment of inflammatory diseases including infectious diseases, acute or chronic inflammation (acute gouty arthritis or rheumatoid arthritis), cardiovascular disease, atheromatosis, diabetes, obesity, tissue injury and tumors. It has significant value in the prevention and treatment of these serious and painful diseases. Graphical abstract.

Serial Crystallography for Structure-Based Drug Discovery.

Rational drug discovery has greatly accelerated the development of safer and more efficacious therapeutics, assisted significantly by insights from experimentally determined 3D structures of ligands in complex with their targets. Serial crystallography (SX) with X-ray free-electron lasers has enabled structural determination using micrometer- or nanometer-size crystals. This technology, applied in the past decade to solve structures of notoriously difficult-to-study drug targets at room temperature, has now been adapted for use in synchrotron radiation facilities. Ultrashort time scales allow time-resolved characterization of dynamic structural changes and pave the road to study the molecular mechanisms by 'molecular movie.' This article summarizes the latest progress in SX technology and deliberates its demanding applications in future structure-based drug discovery.

An image-based Pathogen Box screen identifies new compounds with anti-Giardia activity and highlights the importance of assay choice in phenotypic drug discovery.

Giardia duodenalis, the most prevalent human intestinal parasite causes the disease, giardiasis. On an annual basis G. duodenalis infects ~1 billion people, of which ~280 million develop symptomatic disease. Giardiasis can be severe and chronic, causing malnutrition, stunted growth and poor cognitive development in children. Current treatment options rely on drugs with declining efficacy and side-effects. To improve the health and well-being of millions of people world-wide, new anti-Giardia drugs with different modes of action to currently used drugs are required. The Medicines for Malaria Venture's Pathogen Box, a collection of bio-active compounds specifically chosen to stimulate infectious disease drug discovery, represents an opportunity for the discovery of new anti-Giardia agents. While the anti-Giardia activity of Pathogen Box compounds has been reported, this work failed to identify known anti-Giardia controls within the compound set. It also reported the activity of compounds previously screened and shown to be inactive by others, suggesting data may be inaccurate. Given these concerns the anti-Giardia activity of Pathogen Box compounds was re-assessed in the current study. Data from this work identified thirteen compounds with anti-Giardia IC50 values ≤2 µM. Five of these compounds were reference compounds (marketed drugs with known anti-microbial activity), or analogues of compounds with previously described anti-Giardia activity. However, eight, including MMV676358 and MMV028694, which demonstrated potent sub-µM IC50s against assemblage A, B and metronidazole resistant parasites (0.3 µM and 0.9 µM respectively), may represent new leads for future drug development. Interestingly, only four of these compounds were identified in the previously reported Pathogen Box screen highlighting the importance of assay selection and design when assessing compounds for activity against infectious agents.

Design, Synthesis and Characterization of a New Series of Fluorescent Metabotropic Glutamate Receptor Type 5 Negative Allosteric Modulators.

In recent years, new drug discovery approaches based on novel pharmacological concepts have emerged. Allosteric modulators, for example, target receptors at sites other than the orthosteric binding sites and can modulate agonist-mediated activation. Interestingly, allosteric regulation may allow a fine-tuned regulation of unbalanced neurotransmitter' systems, thus providing safe and effective treatments for a number of central nervous system diseases. The metabotropic glutamate type 5 receptor (mGlu5R) has been shown to possess a druggable allosteric binding domain. Accordingly, novel allosteric ligands are being explored in order to finely regulate glutamate neurotransmission, especially in the brain. However, before testing the activity of these new ligands in the clinic or even in animal disease models, it is common to characterize their ability to bind mGlu5Rs in vitro. Here, we have developed a new series of fluorescent ligands that, when used in a new NanoBRET-based binding assay, will facilitate screening for novel mGlu5R allosteric modulators.

Comprehensive online multicolumn two-dimensional liquid chromatography-diode array detection-mass spectrometry workflow as a framework for chromatographic screening and analysis of new drug substances.

The analysis of complex mixtures of closely related species is quickly becoming a bottleneck in the development of new drug substances, reflecting the ever-increasing complexity of both fundamental biology and the therapeutics used to treat disease. Two-dimensional liquid chromatography (2D-LC) is emerging as a powerful tool to achieve substantial improvements in peak capacity and selectivity. However, 2D-LC suffers from several limitations, including the lack of automated multicolumn setups capable of combining multiple columns in both dimensions. Herein, we report an investigation into the development and implementation of a customized online comprehensive multicolumn 2D-LC-DAD-MS setup for screening and method development purposes, as well as analysis of multicomponent biopharmaceutical mixtures. In this study, excellent chromatographic performance in terms of selectivity, peak shape, and reproducibility were achieved by combining reversed-phase (RP), strong cation exchange (SCX), strong anion exchange (SAX), and size exclusion chromatography (SEC) using sub-2-µm columns in the first dimension in conjunction with several 3.0 mm × 50 mm RP columns packed with sub-3-µm fully porous particles in the second dimension. Multiple combinations of separation modes coupled to UV and MS detection are applied to the LC × LC analysis of a protein standard mixture, intended to be representative of protein drug substances. The results reported in this study demonstrate that our automated online multicolumn 2D-LC-DAD-MS workflow can be a powerful tool for comprehensive chromatographic column screening that enables the semi-automated development of 2D-LC methods, offering the ability to streamline full visualization of sample composition for an unknown complex mixture while maximizing chromatographic orthogonality. Graphical Abstract.

Current developments in LC-MS for pharmaceutical analysis.

Liquid chromatography (LC) based techniques in combination with mass spectrometry (MS) detection have had a large impact on the development of new pharmaceuticals in the past decades. Continuous improvements in mass spectrometry and interface technologies, combined with advanced liquid chromatographic techniques for high-throughput qualitative and quantitative analysis, have resulted in a wider scope of applications in the pharmaceutical field. LC-MS tools are increasingly used to analyze pharmaceuticals across a variety of stages in their discovery and development. These stages include drug discovery, product characterization, metabolism studies (in vitro and in vivo) and the identification of impurities and degradation products. The increase in LC-MS applications has been enormous, with retention times and molecular weights (and related fragmentation patterns) emerging as crucial analytical features in the drug development process. The goal of this review is to give an overview of the main developments in LC-MS based techniques for the analysis of small pharmaceutical molecules in the last decade and give a perspective on future trends in LC-MS in the pharmaceutical field.

gQSPSim: A SimBiology-Based GUI for Standardized QSP Model Development and Application.

Quantitative systems pharmacology (QSP) models are often implemented using a wide variety of technical workflows and methodologies. To facilitate reproducibility, transparency, portability, and reuse for QSP models, we have developed gQSPSim, a graphical user interface-based MATLAB application that performs key steps in QSP model development and analyses. The capabilities of gQSPSim include (i) model calibration using global and local optimization methods, (ii) development of virtual subjects to explore variability and uncertainty in the represented biology, and (iii) simulations of virtual populations for different interventions. gQSPSim works with SimBiology-built models using components such as species, doses, variants, and rules. All functionalities are equipped with an interactive visualization interface and the ability to generate presentation-ready figures. In addition, standardized gQSPSim sessions can be shared and saved for future extension and reuse. In this work, we demonstrate gQSPSim's capabilities with a standard target-mediated drug disposition model and a published model of anti-proprotein convertase subtilisin/kexin type 9 (PCSK9) treatment of hypercholesterolemia.

A homogeneous bioluminescent immunoassay to probe cellular signaling pathway regulation.

Monitoring cellular signaling events can help better understand cell behavior in health and disease. Traditional immunoassays to study proteins involved in signaling can be tedious, require multiple steps, and are not easily adaptable to high throughput screening (HTS). Here, we describe a new immunoassay approach based on bioluminescent enzyme complementation. This immunoassay takes less than two hours to complete in a homogeneous "Add and Read" format and was successfully used to monitor multiple signaling pathways' activation through specific nodes of phosphorylation (e.g pIκBα, pAKT, and pSTAT3). We also tested deactivation of these pathways with small and large molecule inhibitors and obtained the expected pharmacology. This approach does not require cell engineering. Therefore, the phosphorylation of an endogenous substrate is detected in any cell type. Our results demonstrate that this technology can be broadly adapted to streamline the analysis of signaling pathways of interest or the identification of pathway specific inhibitors.

Towards chamber specific heart-on-a-chip for drug testing applications.

Modeling of human organs has long been a task for scientists in order to lower the costs of therapeutic development and understand the pathological onset of human disease. For decades, despite marked differences in genetics and etiology, animal models remained the norm for drug discovery and disease modeling. Innovative biofabrication techniques have facilitated the development of organ-on-a-chip technology that has great potential to complement conventional animal models. However, human organ as a whole, more specifically the human heart, is difficult to regenerate in vitro, in terms of its chamber specific orientation and its electrical functional complexity. Recent progress with the development of induced pluripotent stem cell differentiation protocols, made recapitulating the complexity of the human heart possible through the generation of cells representative of atrial & ventricular tissue, the sinoatrial node, atrioventricular node and Purkinje fibers. Current heart-on-a-chip approaches incorporate biological, electrical, mechanical, and topographical cues to facilitate tissue maturation, therefore improving the predictive power for the chamber-specific therapeutic effects targeting adult human. In this review, we will give a summary of current advances in heart-on-a-chip technology and provide a comprehensive outlook on the challenges involved in the development of human physiologically relevant heart-on-a-chip.

Do We have a Satisfactory Cell Viability Assay? Review of the Currently Commercially-Available Assays.

Cell-based assays are an important part of the drug discovery process and clinical research. One of the main hurdles is to design sufficiently robust assays with adequate signal to noise parameters while maintaining the inherent physiology of the cells and not interfering with the pharmacology of target being investigated. A plethora of assays that assess cell viability (or cell heath in general) are commercially available and can be classified under different categories according to their concepts and principle of reactions. The assays are valuable tools, however, suffer from a large number of limitations. Some of these limitations can be procedural or operational, but others can be critical as those related to a poor concept or the lack of proof of concept of an assay, e.g. those relying on differential permeability of dyes in-and-out of viable versus compromised cell membranes. While the assays can differentiate between dead and live cells, most, if not all, of them can just assess the relative performance of cells rather than providing a clear distinction between healthy and dying cells. The possible impact of relatively high molecular weight dyes, used in most of the assay, on cell viability has not been addressed. More innovative assays are needed, and until better alternatives are developed, setup of current cell-based studies and data interpretation should be made with the limitations in mind. Negative and positive control should be considered whenever feasible. Also, researchers should use more than one orthogonal method for better assessment of cell health.

Integration of micro-fractionation, high-performance liquid chromatography-ultraviolet detector-charged aerosol detector-mass spectrometry analysis and cellular dynamic mass redistribution assay to accelerate alkaloid drug discovery.

Natural products, including alkaloids, are important resources for new drugs. However, in today's high throughput screening (HTS) environment, natural product drug discovery programs are challenged for their low efficiency. In order to adapt to current HTS models, we here developed a rapid, sample-saving and miniaturized paradigm that seamlessly integrated alkaloid micro-fractionation, quantitative analysis, qualitative analysis and phenotypic screening. In the work, alkaloid samples were analyzed and fractionated on an analytical charged C18 column (150 × 4.6 mm, i.d.), and fraction qualities were determined by a charged aerosol detector (CAD). Fraction activities on dopamine D2 receptor were screened by cellular dynamic mass redistribution (DMR) assay and active fractions were further characterized by high-resolution mass spectrometry (MS). The whole workflow was first validated by mixed standard for accuracy, and then by 300 µg of Corydalis yanhusuo extract for its feasibility in complex samples. Finally, the method was applied for sample prioritization in four papaveraceae family plants and 21 compounds were predicted to be active, and Corydalis yanhusuo and Corydalis decumbens were determined as promising species for activity tracking. Overall, these results highlighted the feasibility of this miniatured and integrated model in rapid alkaloid screening. Advantages of this workflow were: first, the highly efficient separation method accelerated alkaloid fractionation; second, the analytical and biological test were conducted on the same scale; third, the quantification method ensured accurate screening on microscale; last, the combination of MS analysis and data mining strategy accelerated the decision-making process in the primary screening.