Drug Discovery Applications Research Articles

Enantioselective 1,4-Borylamination via Copper-Catalyzed Cascade Hydroborylation and Hydroamination of Arylidenecyclopropanes.

Compounds bearing both boryl and amino groups at distal positions are invaluable synthons for synthesizing pharmaceuticals, drug candidates, and natural products, but their catalytic enantioselective synthesis remains rarely explored. We report the first enantioselective 1,4-borylamination reaction through a copper-catalyzed cascade hydroborylation and hydroamination of arylidenecyclopropanes. This reaction combines four readily available components in a highly chemo-, site-, and enantioselective fashion (>20:1 r.r. and up to 99% ee), yielding a diverse array of synthetically valuable enantioenriched 4-amino alkylboronates. The versatile utility of these products is highlighted by their diverse transformations and wide applications in pharmaceutical synthesis and drug discovery. Preliminary mechanistic studies were conducted to elucidate the operative reaction pathway, intermediates, and origins of its high chemo- and site-selectivity.

Design and Evaluation of Azaspirocycles as RNA binders.

This study presents efficient synthetic pathways for preparing novel azaspirocycles. These methodologies involve functionalizing key bicyclic hydrazines with a substituent on one of their bridgehead carbon atoms. The desired spirocyclic cores were successfully obtained through double reductive amination reactions, intramolecular cyclizations, and cleavages of the N-N bond. The isolated molecules possess unique three-dimensional structures, suggesting potential applications in medicinal chemistry and drug discovery. With the growing interest in targeting nucleic acids as a complementary approach to protein-targeting strategies for developing novel active compounds, we investigated the potential of the synthesized azaspirocycles as RNA binders. As a proof of concept, we highlight the promising activity of some compounds as strong binders of HIV-1 TAR RNA and inhibitors of Tat/TAR interactions.

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in tumorigenesis and anticancer drug resistance, making it a critical target for drug design. miR21 biogenesis involves precise biochemical pathways, including the cleavage of its precursor, pre-miR21, by the enzyme Dicer. The present work investigates the conformational dynamics of pre-miR21, focusing on the role of adenine29 in switching between Dicer-binding-prone and inactive states. We also investigated the effect of L50, a cyclic peptide binder of pre-miR21 and a weak inhibitor of its processing. Using time series data and our novel collective variable-based enhanced sampling technique, OneOPES, we simulated these conformational changes and assessed the effect of L50 on the conformational plasticity of pre-miR21. Our results provide insight into peptide-induced conformational changes and pave the way for the development of a computational platform for the screening of inhibitors of pre-miR21 processing that considers RNA flexibility, a stepping stone for effective structure-based drug design, with potentially broad applications in drug discovery.

Unnatural Amino Acids: Strategies, Designs, and Applications in Medicinal Chemistry and Drug Discovery.

Peptides can operate as therapeutic agents that sit within a privileged space between small molecules and larger biologics. Despite examples of their potential to regulate receptors and modulate disease pathways, the development of peptides with drug-like properties remains a challenge. In the quest to optimize physicochemical parameters and improve target selectivity, unnatural amino acids (UAAs) have emerged as critical tools in peptide- and peptidomimetic-based drugs. The utility of UAAs is illustrated by clinically approved drugs such as methyldopa, baclofen, and gabapentin in addition to small drug molecules, for example, bortezomib and sitagliptin. In this Perspective, we outline the strategy and deployment of UAAs in FDA-approved drugs and their targets. We further describe the modulation of the physicochemical properties in peptides using UAAs. Finally, we elucidate how these improved pharmacological parameters and the role played by UAAs impact the progress of analogs in preclinical stages with an emphasis on the role played by UAAs.

Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive data set comprising parameters across multiple species. Building upon this groundwork, we introduced the PKStack ensemble model to predict PK parameters across diverse species. PKStack integrates a variety of base models and includes uncertainty in its predictions. We also manually collected PK data from animals as an external test set. We predicted a total of 45 tasks for nine PK parameters in five species, and in general, the prediction accuracy was better for intravenous injections, including parameters such as human Vd (R2 = 0.72, RMSE = 0.31), human CL (R2 = 0.52, RMSE = 0.32), and others. In addition to predictive accuracy, we also considered the interpretability of the results and the definition of the model's application domain. Based on the findings, our model has great potential for practical applications in drug discovery.

Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution.

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.

Screening and Analysis of Skin Cancer Treatment Using Biocomponents of Plants Using Backpropagation Neural Networks: A Comprehensive Review

: In recent years, the use of natural compounds derived from plants for the treatment of skin cancer has gained significant attention due to their potential therapeutic effects and minimal side effects. This review focuses on the innovative approach of utilizing biocomponents sourced from plants in combination with backpropagation neural networks (BPNN) for the screening and analysis of skin cancer treatments. The integration of plant-derived compounds and AI-driven algorithms holds promise for enhancing the precision and effectiveness of skin cancer therapies. The review begins by highlighting the escalating global burden of skin cancer and the limitations of conventional treatment approaches. With the rise in concerns about the adverse effects of synthetic drugs, researchers have turned their attention towards exploring the therapeutic potential of plant-derived biocomponents. These natural compounds are known for their rich bioactive constituents that exhibit anti-cancer properties, making them suitable candidates for skin cancer treatment. One of the key challenges in harnessing the potential of plant-derived compounds is the need for accurate screening and analysis of their effects. This is where backpropagation neural networks, a type of artificial neural network, comes into play. These networks can process complex data and recognize intricate patterns, enabling them to predict the efficacy of various biocomponents in combating skin cancer. The review delves into the functioning of BPNN and its applications in drug discovery and treatment evaluation. Furthermore, the review explores several case studies that demonstrate the successful integration of plant-derived compounds with BPNN in the context of skin cancer treatment. These studies provide evidence of how this synergistic approach can lead to improved treatment outcomes by minimizing adverse effects and maximizing therapeutic benefits. The methodology section discusses the steps involved in training the neural network using relevant datasets and optimizing its performance for accurate predictions. While the integration of plant-derived compounds and BPNN shows great promise, the review also addresses the existing challenges and limitations. These include the need for comprehensive and standardized datasets, potential biases in training data, and the complexity of neural network architectures. The regulatory considerations surrounding plant-based therapies are also discussed, highlighting the importance of rigorous testing and validation.

Induced Pluripotent Stem Cells: A New Dawn for the Treatment of Ischemic Cardiomyopathy

Ischemic cardiomyopathy, a leading cause of heart failure globally, arises from the irreversible damage inflicted upon cardiac tissue following myocardial infarction. The limited regenerative capacity of the adult human heart necessitates the exploration of novel therapeutic strategies to restore cardiac function and improve outcomes. Induced pluripotent stem cells (iPSCs), with their remarkable ability to differentiate into virtually any cell type in the body, have emerged as a beacon of hope for regenerative medicine, including the treatment of ischemic cardiomyopathy. This review delves into the pathophysiology of ischemic cardiomyopathy, highlighting the cellular and molecular mechanisms underlying cardiac dysfunction. We provide a comprehensive overview of the current state of iPSC technology, focusing on its potential applications in disease modeling, drug discovery, and cell-based therapies for cardiac regeneration. Furthermore, we discuss the challenges and opportunities associated with translating iPSC-based therapies to the clinic, emphasizing the need for rigorous preclinical studies and well-designed clinical trials to ensure safety and efficacy. Finally, we offer perspectives on the future directions of iPSC research in the context of ischemic cardiomyopathy, highlighting the transformative potential of this technology to revolutionize cardiovascular medicine and usher in an era of personalized regenerative therapies for patients with heart failure.

Efficient Synthesis of 2‐Arylpropionitriles Via Selective Monomethylation of Aryl Acetonitriles Using an Easy to Handle Methylation Agent

AbstractA convenient and safe methylation protocol employing quaternary ammonium salts (PhMe3NI) as alternative methylating agents for the selective α‐methylation of arylacetonitriles is presented. This approach allows for the selective α‐methylation of arylacetonitriles, overcoming the limitations of existing techniques, while offering a practical and sustainable solution for late‐stage functionalization in medicinal chemistry. The straightforward and safe nature of this methodology makes it particularly well‐suited for applications in drug discovery. In our report, we present a diverse set of 18 examples, achieving yields of up to 76 %.

Robust whole‐cell‐based chemoenzymatic synthesis of site‐selective deuterated α‐hydroxy acids and α‐amino acids

Deuterated hydroxyl acids and amino acids have been widely utilized in life science, biochemistry and drug development. Site‐selective and stereoselective synthesis of deuterated hydroxyl acids and amino acids remains a significant challenge. Here, we report the development of a robust whole‐cell‐based chemoenzymatic platform for the synthesis of deuterated hydroxyl acids and amino acids from off‐the‐shelf aldehydes in high yields with excellent selectivities and levels of deuteration. The platform delivers products with diverse scaffolds and deuteration patterns, as well as broad scopes with both aromatic and aliphatic side chains. The application of the platform was demonstrated by the concise synthesis of a deuterium‐containing antiparkinson's disease candidate. This platform provides a concrete foundation for accessing amino acid isotopologs for potential applications in research and drug discovery and development.

Modeling the contribution of cardiac fibroblasts in dilated cardiomyopathy using induced pluripotent stem cells (iPSCs).

Fibrosis is implicated in nearly all forms of cardiomyopathy and significantly influences disease severity and outcomes. The primary cell responsible for fibrosis is the cardiac fibroblast, which remains understudied relative to cardiomyocytes in the context of cardiomyopathy. The development of induced pluripotent stem cell-derived cardiac fibroblasts (iPSC-CFs) allows for the modeling of patient-specific disease characteristics and provides a scalable source of fibroblasts. iPSC-CFs are invaluable for understanding molecular pathways that affect disease progression and outcomes. This review explores various aspects of cardiomyopathy, with a focus on dilated cardiomyopathy (DCM), that can be modeled using iPSC-CFs and their application in drug discovery, given the current lack of approved therapies for cardiac fibrosis. We examine how iPSC-CFs can be utilized to study heart development, fibroblast heterogeneity, and activation, with the ultimate goal of developing better therapies for patients with cardiomyopathies. Significance Statement Here, we explore how iPSC-CFs can be used to study the fibrotic component of DCM. Most research has focused on cardiomyocytes, despite the significant contribution of fibroblasts to disease outcomes. iPSC-CFs serve as a valuable tool to elucidate molecular pathways leading to fibrosis, and paracrine interactions with cardiomyocytes, which are not fully understood. Gaining insights into these events could aid in the development of new therapies and enable the use of patient-derived iPSC-CFs for precision medicine, ultimately improving patient outcomes.

Oxadiazolone-Based Aromatic Annulations: A Nitrenoid Precursor for Tricyclic Aminoheterocycles.

Nitrogen-containing heterocycles are present in most approved drugs, reflecting the significance of their synthetic strategies. By utilizing oxadiazolone as a nitrenoid (nitrene-like) precursor, we have developed a general strategy for the annulation with nucleophilic heterocycles to access various polycyclic aminoheterocycles. We have discovered that 2-pyrryl-substituted substrates undergo a rearrangement, which indicates a spirocyclization-migration pathway. Given their fluorescence and biological activity as kinase hinge binders, these fragment-like aminoheterocycles represent potential starting points for applications in chemical biology and drug discovery.

Molecular Docking in Drug Discovery: Techniques, Applications, and Advancements.

The primary objective of this study is to conduct a comprehensive review of the significance of molecular docking in the field of drug discovery. This includes an examination of the various approaches and methods used in molecular docking, as well as an exploration of the techniques used for interpreting and validating docking results. To gather relevant data, a systematic search was conducted using Web of Science, PubMed, and Google Scholar. The search focused on articles related to molecular docking methodologies and their applications in drug discovery. Additionally, alternative techniques that can be used for more precise simulations of ligand-protein interactions were also considered. Molecular docking has proven to be an incredibly rich and valuable process in the field of drug discovery. Its flexibility allows for the incorporation of advanced computational techniques, thereby enhancing the reliability and efficiency of drug discovery processes. The results of the study highlights the significant strides made in the field of molecular docking, demonstrating its potential to revolutionize drug discovery. Molecular docking continues to evolve, with new advancements being made regularly. Despite the challenges faced, these advancements have significantly contributed to the enhancement of molecular docking, solidifying its position as a crucial tool in the field of drug discovery.

Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs

The expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent of artificial intelligence (AI), there has been a transformative shift in the generation of new peptide drugs. AI offers a range of computational tools and algorithms that enables researchers to accelerate the therapeutic peptide pipeline. This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations. Additionally, it presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Anion-driven C[sbnd]F bond cleavage of trifluoromethyl N-aryl hydrazones toward the assembly of N-heterocycles

Herein we report an efficient and convenient method for synthesizing 1H-benzo[d]imidazoles via a base-promoted reaction of readily available α-CF3 ketone-derived hydrazones with o-phenylenediamines. The key innovation is the anion-driven triple-cleavage of the CF bond in the CF3 group, which acts as a C1 synthon at the 2-position of the 1H-benzo[d]imidazole. This novel approach simplifies the synthesis and can be extended to the preparation of benzo[d]oxazoles and benzo[d]thiazoles. Our method offers a versatile and robust platform for the synthesis of important heterocyclic compounds, with potential applications in medicinal chemistry and drug discovery.

Empirically establishing drug exposure records directly from untargeted metabolomics data.

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N -acyl lipid changes in a human immunodeficiency virus cohort. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

A critical review on approaches to generate and validate virtual population for physiologically based pharmacokinetic models: Methodologies, case studies and way forward.

In silico modeling and simulation techniques such as physiologically based pharmacokinetic (PBPK) and physiologically based biopharmaceutics modeling (PBBM) have demonstrated various applications in drug discovery and development. Virtual bioequivalence leverages these computation tools to predict bioequivalence between reference and test formulations thereby demonstrating possibilities to reduce human studies. A pre-requisite for virtual bioequivalence is development of validated virtual population that depicts the same variability as that of observed in clinic. This development, validation and optimization of virtual population is a key attribute of virtual bioequivalence based on which conclusion of bioequivalence is made. Various strategies for optimization of virtual population based on appropriate considerations of physicochemical, physiological and disposition aspects are demonstrated with the help of six diverse case studies of immediate and modified release formulations. Once the virtual population is optimized to match in vivo variability, it can be used for various applications such as biowaivers, dissolution specification justification, f2 mismatch, establishing dissolution safe space, etc. In this review article, we attempted to describe various methodologies and approaches for optimization of virtual population using Gastroplus. Strategies based on optimization of virtual population with emphasis on specific and sensitive parameters were portrayed. We have further elucidated considerations related to study design, in vivo variability, sample size for optimization of virtual population from Gastroplus perspective. We believe that this review article provides a step-by-step process for virtual population optimization for interest of biopharmaceutics modeling scientists in order to ensure reliable and credible physiological models.

Photoredox/Copper-Cocatalyzed Domino Annulation of Oxime Esters and NH4SCN: Access to Fully Substituted 2-Aminothiazoles.

Domino cyclization of oxime esters and NH4SCN facilitated by photoredox and copper cocatalysis has been established. Various structurally diverse fully substituted 2-aminothiazoles have been obtained in good yields at room temperature. It is featured by mild conditions, favorable functional group tolerance, and wide substrate scope. The present reaction is amenable to gram-scale synthesis, which is expected to find potential applications in organic synthesis and drug discovery. A plausible reaction mechanism is proposed.

Alternative Method for Obtaining Human-Induced Pluripotent Stem Cell Lines and Three-Dimensional Growth: A Simplified, Passage-Free Approach that Minimizes Labor.

Induced pluripotent stem cells (iPSCs) hold significant promise for numerous applications in regenerative medicine, disease modeling, and drug discovery. However, the conventional workflow for iPSC generation, with cells grown under two-dimensional conditions, presents several challenges, including the need for specialized scientific skills such as morphologically assessing and picking colonies and removing differentiated cells during the establishment phase. Furthermore, maintaining established iPSCs in three-dimensional culture systems, while offering scalability, necessitates an enzymatic dissociation step for their further growth in a complex and time-consuming protocol. In this study, we introduce a novel approach to address these challenges by reprogramming somatic cells grown under three-dimensional conditions as spheres using a bioreactor, thereby eliminating the need for two-dimensional culture and colony picking. The iPSCs generated in this study were maintained under three-dimensional conditions simply by transferring spheres to the next bioreactor, without the need for an enzymatic dissociation step. This streamlined method simplifies the workflow, reduces technical variability and labor, and paves the way for future advancements in iPSC research and its wider applications. Key features • Establishment of induced pluripotent stem cells in a three-dimensional environment. • Maintenance and cryopreservation of iPSCs without the need for a dissociation step.