Rapid LC-MS Based High-Throughput Screening Method, Affording No False Positives or False Negatives, Identifies a New Inhibitor for Carbonic Anhydrase

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

Fragment-Based Drug Discovery for Trypanosoma brucei Glycosylphosphatidylinositol-Specific Phospholipase C through Biochemical and WaterLOGSY-NMR Methods

Glycosylphosphatidylinositol-specific phospholipase C (GPI-PLC) of Trypanosoma brucei, the causative protozoan parasite of African trypanosomiasis, is a membrane-bound enzyme essential for antigenic variation, because it catalyses the release of the membrane-bound form of variable surface glycoproteins. Here, we performed a fragment-based drug discovery of TbGPI-PLC inhibitors using a combination of enzymatic inhibition assay and water-ligand observed via gradient spectroscopy (WaterLOGSY) NMR experiment. The TbGPI-PLC was cloned and over-expressed using an Escherichia coli expression system followed by purification using three-phase partitioning and gel filtration. Subsequently, the inhibitory activity of 873 fragment compounds against the recombinant TbGPI-PLC led to the identification of 66 primary hits. These primary hits were subjected to the WaterLOGSY NMR experiment where 10 fragment hits were confirmed to directly bind to the TbGPI-PLC. These included benzothiazole, chlorobenzene, imidazole, indole, pyrazol and quinolinone derivatives. Molecular docking simulation indicated that six of them share a common binding site, which corresponds to the catalytic pocket. The present study identified chemically diverse fragment hits that could directly bind and inhibit the TbGPI-PLC activity which constructed a framework for fragment optimisation or linking towards the design of novel drugs for African trypanosomiasis.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Development and Validation of High-Throughput Bioanalytical Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) Method for the Quantification of Newly Synthesized Antitumor Carbonic Anhydrase Inhibitors in Human Plasma

In the present study, a sensitive and fully validated bioanalytical high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) method has been developed for the quantitative determination of three newly synthesized carbonic anhydrases inhibitors (CAIs) with potential antitumor activity in human plasma. The analytes and the internal standard (IS) were extracted using 1.5 mL acetonitrile from only 450 µL aliquots of human plasma to achieve the desired protein precipitation. Chromatographic separations were achieved on Phenomenex Kinetex^® C₁₈ column (100 × 4.6 mm, 2.6 µm) using a binary gradient elution mode with a run time of less than 6 min. The mobile phase consisted of solvent (A): 0.1% formic acid in 50% methanol and solvent B: 0.1% formic acid in acetonitrile (30:70, v/v), pumped at a flow rate of 0.8 mL/min. Detection was employed using triple quadrupole tandem mass spectrometer (API 3500) equipped with an electrospray ionization (ESI) source in the positive ion mode. Multiple reaction monitoring (MRM) mode was selected for quantitation through monitoring the precursor-to-parent ion transition at m/z 291.9 → 173.0, m/z 396.9 → 225.1, m/z 388.9 → 217.0, and m/z 146.9 → 91.0 for AW-9a, WES-1, WES-2, and Coumarin (IS), respectively. Linearity was computed using the weighted least-squares linear regression method (1/x²) over a concentration range of 1–1000, 2.5–800, and 5–500 ng/mL for AW-9a, WES-1, and WES-2; respectively. The bioanalytical LC-MS/MS method was fully validated as per U.S. Food and Drug Administration (FDA) guidelines with all respect to linearity, accuracy, precision, carry-over, selectivity, dilution integrity, and stability. The proposed LC-MS/MS method was applied successfully for the determination of all investigated drugs in spiked human plasma with no significant matrix effect, which is a crucial cornerstone in further therapeutic drug monitoring of newly developed therapeutic agents.

Solving the Competitive Binding Equilibria between Many Ligands: Application to High-Throughput Screening and Affinity Optimization

Modern small-molecule drug discovery relies on the selective targeting of biological macromolecules by low-molecular weight compounds. Therefore, the binding affinities of candidate drugs to their targets are key for pharmacological activity and clinical use. For drug discovery methods where multiple drug candidates can simultaneously bind to the same target, a competition is established, and the resulting equilibrium depends on the dissociation constants and concentration of all the species present. Such coupling between all equilibrium-governing parameters complicates analysis and development of improved mixture-based, high-throughput drug discovery techniques. In this work, we present an iterative computational algorithm to solve coupled equilibria between an arbitrary number of ligands and a biomolecular target that is efficient and robust. The algorithm does not require the estimation of initial values to rapidly converge to the solution of interest. We explored binding equilibria under ligand/receptor conditions used in mixture-based library screening by affinity selection-mass spectrometry (AS-MS). Our studies support a facile method for affinity-ranking hits. The ranking method involves varying the receptor-to-ligand concentration ratio in a pool of candidate ligands in two sequential AS-MS analyses. The ranking is based on the relative change in bound ligand concentration. The method proposed does not require a known reference ligand and produces a ranking that is insensitive to variations in the concentration of individual compounds, thereby enabling the use of unpurified compounds generated by mixture-based combinatorial synthesis techniques.

Solid-Supported Proteins in the Liquid Chromatography Domain to Probe Ligand-Target Interactions

Ligand-target interactions play a central role in drug discovery processes because these interactions are crucial in biological systems. Small molecules-proteins interactions can regulate and modulate protein function and activity through conformational changes. Therefore, bioanalytical tools to screen new ligands have focused mainly on probing ligand-target interactions. These interactions have been evaluated by using solid-supported proteins, which provide advantages like increased protein stability and easier protein extraction from the reaction medium, which enables protein reuse. In some specific approaches, precisely in the ligand fishing assay, the bioanalytical method allows the ligands to be directly isolated from complex mixtures, including combinatorial libraries and natural products extracts without prior purification or fractionation steps. Most of these screening assays are based on liquid chromatography separation, and the binding events can be monitored through on-line or off-line methods. In the on-line approaches, solid supports containing the immobilized biological target are used as chromatographic columns most of the time. Several terms have been used to refer to such approaches, such as weak affinity chromatography, high-performance affinity chromatography, on-flow activity assays, and high-performance liquid affinity chromatography. On the other hand, in the off-line approaches, the binding event occurs outside the liquid chromatography system and may encompass affinity and activity-based assays in which the biological target is immobilized on magnetic particles or monolithic silica, among others. After the incubation step, the supernatant or the eluate from the binding assay is analyzed by liquid chromatography coupled to various detectors. Regardless of the selected bioanalytical approach, the use of solid supported proteins has significantly contributed to the development of automated and reliable screening methods that enable ligands to be isolated and characterized in complex matrixes without purification, thereby reducing costs and avoiding time-laborious steps. This review provides a critical overview of recently developed assays.

Rapid identification of urokinase plasminogen activator inhibitors from Traditional Chinese Medicines based on ultrafiltration, LC-MS and in silico docking

The urokinase plasminogen activator (uPA) is regarded as the crucial trigger for plasmin generation, which is involved in several diseases especially for neoplasm metastasis. In this study, an efficient approach integrating ultrafiltration, LC/MS, bioassay and in silico docking, was proposed for rapidly detecting uPA ligands from Traditional Chinese Medicines (TCMs). Forty-two TCMs were initially assessed, and as illustrative case studies, Galla Chinensis and Sanguisorbae Radix, which appeared significant inhibitory activities on uPA, were chosen to develpe and verify the strategy. A total of seven uPA ligands were successfully detected and identified. Two of them, pentagalloylglucose and 28-O-β-d-glucopyranosyl pomolic acid, were demonstrated to be potential inhibitors, with IC50 at 1.639 μM and 37.82 μM repectively. Furthermore, a combinatorial compound library screening combined with in silico docking assay, was revealed that ursolic acid (IC50 = 2.623 μM) was also speculated to be a potent parent structure for inhibition of uPA. This approach offers a multidimensional perspective to discover uPA-binding leading compounds from TCMs or other complex mixtures, which would provide an efficient route for drug discovery.