There is an urgent need to develop new anti-tuberculosis (anti-TB) drugs to tackle drug-resistant strains of Mycobacterium tuberculosis (M.tb). Whereas antimicrobial peptides (AMPs) have received attention because of their antibacterial properties, oligo-N-substituted glycines (peptoids) are now seen as favorable alternatives to AMPs because they are more stable and less vulnerable to protease degradation, less expensive to produce, and better suited to potential pharmaceutical adoption and development. In this work, therefore, we designed, synthesized, and screened 22 new α- and β-peptoids against drug susceptible M. tb strain H37Rv using the Microplate Alamar Blue assay (MABA) to evaluate minimum inhibitory concentration (MIC). Eight compounds (JC5, MM2, MM5, MM9, MM10, MM11, JF11, and JF13) had MICs of less than 10 μg/ml, the most potent of which were JC5 and MM2, with MICs of 1.48 μg/ml and 2.97 μg/ml, respectively. JC5 and MM2 also retained potency against strains mono-resistant to isoniazid and rifampin, and against five of the global M. tb clade representatives. Furthermore, peptoids JC5 and MM2 showed minimum bactericidal concentration (MBC) of 3.02 μg/ml and 5.48 μg/ml respectively. Intracellular activity by luminescence showed a macrophage EC90 of less than 10 μg/ml for both JC5 and MM2. In addition, both compounds showed remarkable narrow spectrum activity. Selectivity with respect to Vero cells was modest but sufficient to consider these classes of alpha and beta-peptoids as good leads for further development of anti-TB drugs.

For decades, the discovery of antiparasitics was dominated by whole-organism screening of intact parasite organisms or surrogate parasite models, such as Caenorhabitis elegans, using in vivo animal models or in vitro parasite assays, the latter also known as phenotypic screening. Molecular target-based screening played only a minor role, if at all. While publications using phenotypic screening are abundant in the literature, publications of successful, marketed, antiparasitic drugs discovered using the molecular target-based approach are scarce. This approach, therefore, is often perceived as less relevant for antiparasitic drug discovery than the two other approaches. However, antiparasitics belonging, for example, to the isoxazolines, bispyrazoles, depsipeptides or praziquantel (PZQ) derivatives, imposingly demonstrate the value of this approach, when wisely used in a cooperative manner with phenotypic screening.

In today's information-driven era, machine learning is revolutionizing medicinal chemistry, offering a paradigm shift from traditional, intuition-based, and often bias-prone methods to the prediction of chemical properties without prior knowledge of the basic principles governing drug function. This perspective highlights the growing importance of informatics in shaping the field of medicinal chemistry, particularly through the concept of the "informacophore". The informacophore refers to the minimal chemical structure, combined with computed molecular descriptors, fingerprints, and machine-learned representations of its structure, that are essential for a molecule to exhibit biological activity. Similar to a skeleton key unlocking multiple locks, the informacophore points to the molecular features that trigger biological responses. By identifying and optimizing informacophores through in-depth analysis of ultra-large datasets of potential lead compounds and automating standard parts in the development process, there will be a significant reduction in biased intuitive decisions, which may lead to systemic errors and a parallel acceleration of drug discovery processes.

This study investigated the role of plasma proteins in obesity to identify predictive biomarkers and explore underlying biological mechanisms.

In the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study, 809 proteins were measured in 2045 individuals (564 obese and 1481 non-obese). Multivariate logistic regression adjusted for confounders (basic and full models) was used to identify obesity-associated proteins. Priority-Lasso was applied for feature selection, followed by machine learning models (support vector machine [SVM], random forest [RF], k-nearest neighbour [KNN] and adaptive boosting [Adaboost]) for prediction. Correlation and enrichment analyses were performed to elucidate relationships between protein biomarkers, obesity risk factors and perturbed pathways. Mendelian randomisation (MR) assessed causal links between proteins and obesity.

A total of 16 proteins were identified as significantly associated with obesity through multivariable logistic regression in the basic model and subsequent Priority-Lasso analysis. Enrichment analyses highlighted immune response, lipid metabolism and inflammation regulation were linked to obesity. Machine learning models demonstrated robust predictive performance with area under the curves (AUC) of 0.820 (SVM), 0.805 (RF), 0.791 (KNN) and 0.819 (Adaboost). All 16 proteins correlated with obesity-related risk factors such as blood pressure and lipid levels. MR analysis identified AFM, CRP and CFH as causal and potentially modifiable proteins.

The protein signatures identified in our study showed promising predictive potential for obesity. These findings warrant further investigation to evaluate their clinical applicability, offering insights into obesity prevention and treatment strategies.

Physiologically based pharmacokinetic (PBPK) modeling, a cornerstone of model-informed drug development and model-informed precision dosing, simulates drug disposition in the human body by integrating physiological, biochemical, and physicochemical parameters. While PBPK modeling has advanced globally since the 1970s, China's adoption of this technology has followed a distinctive path, characterized by accelerated growth over the past 2 decades. This review provides a comprehensive analysis of China's contributions to PBPK modeling, addressing knowledge gaps in publication trends, application domains, and platform preferences. A systematic literature search yielded 266 original PBPK research articles from PubMed up to August 08, 2024. The analysis revealed that drug disposition and drug-drug interaction studies constitute the largest proportion of PBPK analyses in China. Chinese universities and hospitals emerge as the leading contributors to PBPK research among institutions in China. Although established commercial PBPK platform such as GastroPlus and Simcyp remain popular within the Chinese pharmaceutical industry, open-source platforms like PK-Sim are gaining significant traction in PBPK applications across China. This review underscores the transformative potential of PBPK modeling in drug development within China, offering valuable insights into future directions and challenges in the field.

The journey from initial drug discovery to approval for respiratory diseases typically spans approximately 10.4 years and cost over $2.8 billion. This intricate process involves five stages: target identification, therapeutic molecule discovery, preclinical testing, clinical trials, and regulatory approval.

This review examines novel drug discovery strategies for chronic obstructive pulmonary disease (COPD), focusing on advanced in vitro models that replicate human lung conditions for accurate drug testing according to the following search string: discovery AND strategy AND COPD. It explores targeted molecular therapies, structure-based drug design, and drug repurposing approaches facilitated by computational analysis. The significance of personalized medicine in tailoring treatments for diverse COPDs is emphasized, highlighting the complexity of the disease and the necessity of these innovative methodologies to improve therapeutic outcomes.

COPD remains a challenging area, with a significant unmet medical need. Despite previous efforts, few effective therapies exist. Innovative in vitro models, targeted molecular therapies, and drug repurposing strategies are showing promise. Emphasizing advanced preclinical models and repurposing existing drugs could transform treatment paradigms, promoting more effective therapies for complex diseases like COPD. These innovations hold potential for enhancing drug discovery efficiency, leading to personalized and precision medicine approaches.

In drug discovery, various approaches exist to find compounds that alter the different states in living organisms. There are two fundamental discovery strategies regarding the mechanism of action: target-based and phenotypic methods. Both have strengths and weaknesses in assay development, target selection, target validation and structure optimization. While phenotypic screening can identify chemical starting points with the desired phenotype, it is typically difficult to carry out efficient, structure-based optimization without confirming the mechanism of action of such hits. It is therefore critical to uncover the targets behind the phenotype. Target deconvolution is typically carried out by a set of highly selective compounds, where each ligand is associated with a particular target. Hits of such a high-selectivity set can provide valuable information on the phenotype's underlying targets and may also enable novel target-based therapeutic strategies. Consequently, there is a continuously high demand for novel highly-selective tool compounds for target deconvolution. In this work, the ChEMBL database, comprising over 20 million bioactivity data, was mined to identify the most selective novel ligands for a diverse set of targets. A novel method for the automated selection of such high-selectivity ligands is presented. Using these high-selectivity compounds in phenotypic screening campaigns can provide a valuable preliminary direction during target deconvolution. 87 representative compounds were purchased and screened against 60 cancer cell lines. Several compounds were found to possess selective inhibition of cell growth of a few distinct cell lines. The phenotypic assay results, along with the nanomolar activities of individual proteins obtained from the ChEMBL database suggest some novel mechanisms of action for anti-cancer drug discovery.

Simplified analogs of cortistatin A were synthesized and biologically evaluated to develop novel antitumor substances that target angiogenesis. To analyze the effect of substituents at positions corresponding to C-2 and/or C-4 of the A-ring, various pyrone- or pyridone-embedded analogs were designed and synthesized. Among the prepared analogs, the pyridone analog 19 bearing a methyl group at C-2 and a hydroxyl group at C-4 showed potent and selective growth inhibitory activity against human umbilical vein endothelial cells (HUVECs, IC50 = 0.001 µM, selective index over that against human epidermoid carcinoma KB3-1 cells = 6400), exceeding those of natural products. The analog 19 of oral administration exhibited excellent in vivo antitumor activity in mice subcutaneously inoculated with sarcoma S180 cells.

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model compounds and increase true discovery rates, but they are typically black box in nature and do not generate specific chemical insights. Explainable deep learning aims to 'open up' the black box by providing generalizable and human-understandable reasoning for model predictions. These explanations can augment molecular discovery by identifying structural classes of compounds with desired activity in lieu of lone compounds. Additionally, these explanations can guide hypothesis generation and make searching large chemical spaces more efficient. Here we present an explainable deep learning platform that enables vast chemical spaces to be mined and the chemical substructures underlying predicted activity to be identified. The platform relies on Chemprop, a software package implementing graph neural networks as a deep learning model architecture. In contrast to similar approaches, graph neural networks have been shown to be state of the art for molecular property prediction. Focusing on discovering structural classes of antibiotics, this protocol provides guidelines for experimental data generation, model implementation and model explainability and evaluation. This protocol does not require coding proficiency or specialized hardware, and it can be executed in as little as 1-2 weeks, starting from data generation and ending in the testing of model predictions. The platform can be broadly applied to discover structural classes of other small molecules, including anticancer, antiviral and senolytic drugs, as well as to discover structural classes of inorganic molecules with desired physical and chemical properties.

Receptors are crucial for converting chemical and environmental signals into cellular responses, making them prime targets in drug discovery, with about 70% of drugs targeting these receptors. Biased signaling, or functional selectivity, has revolutionized drug development by enabling precise modulation of receptor signaling pathways. This concept is more firmly established in G protein-coupled receptor and has now been applied to other receptor types, including ion channels, receptor tyrosine kinases, and nuclear receptors. Advances in structural biology have further refined our understanding of biased signaling. This targeted approach enhances therapeutic efficacy and potentially reduces side effects. Numerous biased drugs have been developed and approved as therapeutics to treat various diseases, demonstrating their significant therapeutic potential. This review provides a comprehensive overview of biased signaling in drug discovery and disease treatment, highlighting recent advancements and exploring the therapeutic potential of these innovative modulators across various diseases.

Discovering therapeutic molecules requires the integration of both phenotype-based drug discovery (PDD) and target-based drug discovery (TDD). However, this integration remains challenging due to the inherent heterogeneity, noise, and bias present in biomedical data. In this study, Knowledge-Guided Drug Relational Predictor (KGDRP), a graph representation learning approach is developed that effectively integrates multimodal biomedical data, including network data containing biological system information, gene expression data, and sequence data that incorporates chemical molecular structures, all within a heterogeneous graph (HG) structure. By incorporating biomedical HG (BioHG) into a heterogeneous graph neural network (HGNN)-based architecture, KGDRP exhibits a remarkable 12% improvement compared to previous methods in real-world screening scenarios. Notably, the biology-informed representation, derived from KGDRP, significantly enhance target prioritization by 26% in drug target discovery. Furthermore, zero-shot evaluation on COVID-19 exhibited a notably higher success rate in identifying diverse potential drugs. The utilization of BioHG facilitates a unique KGDRP-based analysis of cell-target-drug interactions, thereby enabling the elucidation of drug mechanisms. Overall, KGDRP provides a robust infrastructure for the seamlessly integration of multimodal data and biomedical networks, effectively accelerating PDD, guiding therapeutic target discovery, and ultimately expediting therapeutic molecule discovery.

While the excessive use of broad-spectrum antibiotics is a major driver of the global antibiotic resistance crisis, more selective therapies remain unavailable for the majority of bacterial pathogens. This includes the obligate intracellular bacterial pathogens of the genus Chlamydia, which cause millions of urogenital, ocular, and respiratory infections each year. Conducting a comprehensive search of the chemical space for novel antichlamydial activities, we identified over 60 compounds that are chemically diverse, structurally distinct from known antibiotics, non-toxic to human cells, and highly potent in preventing the growth of Chlamydia trachomatis in cell cultures. Some blocked C. trachomatis development reversibly, while others eradicated both established and persistent infections in a bactericidal manner. The top molecules displayed compelling selectivity, yet broad activity against diverse Chlamydia strains and species, including both urogenital and ocular serovars of C. trachomatis, as well as Chlamydia muridarum and Chlamydia caviae. Some compounds also displayed synergies with clinically used antibiotics. Critically, we found the most potent antichlamydial compound to inhibit fatty acid biosynthesis via covalent binding to the active site of Chlamydia FabH, identifying a new mechanism of FabH inhibition and highlighting a possible way to selectively treat Chlamydia infections.

First-in-class drug discovery (FICDD) offers novel therapies, new biological targets and mechanisms of action (MOAs) toward targeting various diseases and provides opportunities to understand unexplored biology and to target unmet diseases. Current screening approaches followed in FICDD for discovery of hit and lead molecules can be broadly categorized and discussed under phenotypic drug discovery (PDD) and target-based drug discovery (TBDD). Each category has been further classified and described with suitable examples from the literature outlining the current trends in screening approaches applied in small molecule drug discovery (SMDD). Similarly, recent applications of functional genomics, structural biology, artificial intelligence (AI), machine learning (ML), and other such advanced approaches in FICDD have also been highlighted in the article. Further, some of the current medicinal chemistry strategies applied during discovery of hits and optimization studies such as hit-to-lead (HTL) and lead optimization (LO) have been simultaneously overviewed in this article.

Traditional phenotypic drug discovery platforms have suffered from poor scalability and a lack of mechanistic understanding of newly discovered phenotypic probes. To address this, we created Endo- GeneScreen (EGS), a high-throughput enabled screening platform that identifies bioactive small molecules capable of regulating endogenous protein expression encoded by any preselected target gene within a biologically appropriate context. As a proof-of-concept, EGS successfully identified drug candidates that up-regulate endogenous expression of neuronal Syngap1, a gene that causes a neurodevelopmental disorder when haploinsufficient. For example, SR-1815, a previously unknown and undescribed kinase inhibitor, alleviated major cellular consequences of Syngap1 loss-of-function by restoring normal SynGAP protein levels and dampening neuronal hyperactivity within haploinsufficient neurons. Moreover, we demonstrate that EGS assays accelerate preclinical development of identified drug candidates and facilitate mode-of-action deconvolution studies. Thus, EGS identifies first-in-class bioactive small molecule probes that promote biological discovery and precision therapeutic development.

The manufacture of high-performance liquid chromatography (HPLC) medium has long been viewed as an art rather than science; this raised a great challenge in securing separation consistency, method transferability, and scaling-up in purification of biomolecules. Herein, we report a large scale layer-by-layer manufacturing strategy for a high performance chromatography medium utilizing 3D-printing technology. Combining stereolithography 3D printing and porogenic chemistry, the strategy enables parallel production of high-performance separation medium in diverse scales, shapes, and throughput. Between 1,000 printed devices, high performance consistency was demonstrated by column-to-column and batch-to-batch reproducibility (coefficient of variation of retention time, 2.04%). Fast separations of intact proteins were realized in reversed-phase chromatography: within 1 min, resolution > 1.5 was achieved, and nondenatured antibody separation was realized in hydrophobic interaction chromatography. Purification of native proteins was directly amplified by 3 orders of magnitude: 12 mg of hemeproteins was isolated in 8 min at negligible scaling-up cost, supporting liter-scale processing of fermentation within 7 h on one 20 mm i.d. printed column. With advantages in automatic and parallel production capacity, high-fidelity microstructure across dimensions, and highly efficient method transfer and scaling-up, the stereolithographically printed high performance chromatography medium may open a new path to speeding up separation and purification processes from primary analysis to mass-purification of biomolecular entities, as demanded in the biosynthesis and pharmaceutical industries.

Fibrosis is a progressive pathological process that severely impairs normal organ function. Current treatments for fibrosis are extremely limited, with no curative approaches available. In a recent article published in Cell, Zhang and colleagues employed drug screening using ACTA2 reporter iPSC-derived cardiac fibroblasts and identified artesunate as a potent antifibrotic drug by targeting MD2/TLR4 signaling. This study provides new insights into strategies for exploiting existing drugs to treat fibrosis.

Small-molecule compounds exhibit distinct pharmacological properties and clinical effectiveness. Over the past decade, advances in covalent drug discovery have led to successful small-molecule drugs, such as EGFR, BTK, and KRAS (G12C) inhibitors, for cancer therapy. Researchers are paying more attention to refining drug screening methods aiming for high throughput, fast speed, high specificity, and accuracy. Therefore, the discovery and development of small-molecule drugs has been facilitated by significantly reducing screening time and financial resources, and increasing promising lead compounds compared with traditional methods. This review aims to introduce classical and emerging methods for screening small-molecule compounds in targeted cancer therapy. It includes classification, principles, advantages, disadvantages, and successful applications, serving as valuable references for subsequent researchers.

We report the stereoselective total synthesis of kavaratamide A, a linear lipodepsipeptide from the cyanobacterium Moorena bouillonii (collected in Kavaratti, India), and its unnatural C25-epimer. The convergent approach employs Keck asymmetric allylation to construct the chiral β-hydroxy carboxylic acid fragment [(3S)-HDA; 3-hydroxydecanoic acid], while the peptide unit was assembled from L-Val, N-Me-L-Ala, (S)-Hiva, and (S)-iPr-O-Me-pyr using well-orchestrated coupling methods to prevent racemization. Modifications to the Keck allylation conditions enabled the synthesis of the C25-epimer with good yield. Cytotoxicity of kavaratamide A and C25-epi-kavaratamide A, assessed using the MTT assay, demonstrated moderate activity against HepG2 and PANC-1 cell lines.

The process of drug discovery and development is both lengthy and intricate, demanding a substantial investment of time and financial resources. Bioinformatics techniques and tools can not only accelerate the identification of drug targets and the screening and refinement of drug candidates, but also facilitate the characterization of side effects and the prediction of drug resistance. High-throughput data from genomics, transcriptomics, proteomics, and metabolomics make significant contributions to mechanics-based drug discovery and drug reuse. This paper summarizes bioinformatics technologies and tools in drug research and development and their roles and applications in drug research and development, aiming to provide references for the development of new drugs and the realization of precision medicine.

In cancer metastasis, tumor cells condition distant tissues to create a supportive environment, or metastatic niche, by driving the activation of cancer-associated fibroblasts (CAFs). These CAFs remodel the extracellular matrix, creating a microenvironment that supports tumor growth and compromises immune cell function, enabling cancer cells to evade immune detection. Consequently, targeting the activation of CAFs has been proposed as a therapeutic strategy to hinder metastatic spread. Our objective was to develop the first in vitro phenotypic screening assay capable of assessing this activation process.

Human primary lung fibroblasts were co-cultured with highly invasive breast cancer cells (MDA-MB-231) to identify changes in the expression of selected genes using RT-qPCR. An In-Cell ELISA (ICE)-based assay using human lung fibroblasts, MDA-MB-231 cells and human monocytes (THP-1 cells) was developed to measure the activation of CAFs. Another ELISA assay was used to measure released osteopontin.

When lung fibroblast were co-cultured with MDA-MB-231 cells, among the 10 selected genes, the genes for osteopontin (SPP1), insulin like growth factor 1 (IGF1), periostin (POSTN) and α-smooth muscle actin (α-SMA, ACTA2) elicited the greatest fold change (55-, 37-, 8- and 5-fold respectively). Since osteopontin, IGF-1 and periostin are secreted proteins and α-SMA is an intracellular cytoskeleton protein, α-SMA was chosen to be the readout biomarker for the ICE assay. When fibroblasts were co-cultured with MDA-MB-231 cells and monocytes in the 96 well ICE assay, α-SMA expression was increased 2.3-fold yielding a robust Z' of 0.56. A secondary, low throughput assay was developed by measuring the release of osteopontin which showed a 6-fold increase when fibroblasts were co-cultured with MDA-MB-231 cells and monocytes.

This phenotypic assay is the first to measure the activation of CAFs in a 96-well format, making it suitable for medium-to high-throughput screening of potential therapeutic compounds. By focusing on observable cellular phenotypic changes rather than targeting specific molecular pathways, this assay allows for a broader and unbiased identification of compounds capable of modulating CAF activation.

Identification of new antibiotics remains a huge challenge. The last antibiotic of new chemical class and mechanism was discovered more than 30 years ago. Advances since have been largely incremental modifications to a limited number of chemical scaffolds. Discovering and developing truly new antibiotics is challenging: the science is complex, and the development process is time consuming and expensive. Herein, we focus on the discovery phase of modern antibacterial research and development. We argue that antibacterial discovery has been challenged by a poor understanding of bacterial permeability, by generic in vitro conventions that ignore the host, and by the inherent complexity of bacterial systems. Together, these factors have colluded to challenge modern, industrial, and reductionist approaches to antibiotic discovery. Nevertheless, advances in our understanding of many of these obstacles, including a new appreciation for the complexity of both host and pathogen biology, bode well for future efforts.

Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting's ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types.

Endometriosis affects over 190 million women globally, and effective therapies are urgently needed to address the burden of endometriosis on women's health. Using an artificial intelligence (AI)-driven target discovery platform, two unreported therapeutic targets, guanylate-binding protein 2 (GBP2) and hematopoietic cell kinase (HCK) are identified, along with a drug repurposing target, integrin beta 2 (ITGB2) for the treatment of endometriosis. GBP2, HCK, and ITGB2 are upregulated in human endometriotic specimens. siRNA-mediated knockdown of GBP2 and HCK significantly reduced cell viability and proliferation while stimulating apoptosis in endometrial stromal cells. In subcutaneous and intraperitoneal endometriosis mouse models, siRNAs targeting GBP2 and HCK notably reduced lesion volume and weight, with decreased proliferation and increased apoptosis within lesions. Both subcutaneous and intraperitoneal administration of Lifitegrast, an approved ITGB2 antagonist, effectively suppresses lesion growth. Collectively, these data present Lifitegrast as a previously unappreciated intervention for endometriosis treatment and identify GBP2 and HCK as novel druggable targets in endometriosis treatment. This study underscores AI's potential to accelerate the discovery of novel drug targets and facilitate the repurposing of treatment modalities for endometriosis.

Cervical and endometrial cancers pose significant challenges in women's healthcare due to their high mortality rates and limited treatment options. High throughput screening (HTS) of cervical and endometrial cancer in vitro models offers a promising avenue for drug repurposing and broadening patient treatment options. Traditional two-dimensional (2D) cell-based screenings have limited capabilities to capture crucial multicellular interactions, that are improved upon in three dimensional (3D) multicellular tissue engineered models. However, manual fabrication of the 3D platforms is both time consuming and subject to variability. Thus, the goal of this study was to utilize automated cell dispensing to fabricate 3D cell-based HTS platforms using the HP D100 Single Cell Dispenser to dispense cervical and endometrial cancer cells.

We evaluated the effects of automated dispensing of the cancer cell lines by tuning the dispensing protocol to align with cell size measured in solution and the minimum cell number for acceptable cell viability and proliferation. We modified our previously reported coculture models of cervical and endometrial cancer to be in a 384 well plate format and measured microvessel length and cancer cell invasion.

Automatically and manually dispensed cells were directly compared revealing minimal differences between the dispensing methods. These findings suggest that automated dispensing of cancer cells minimally affects cell behavior and can be deployed to decrease in vitro model fabrication time.

By streamlining the manufacturing process, automated dispensing holds promise for enhancing efficiency and scalability of 3D in vitro HTS platforms, ultimately contributing to advancement in cancer research and treatment.

The online version contains supplementary material available at 10.1007/s12195-024-00841-y.

Phenotype-driven approaches identify disease-counteracting compounds by analyzing the phenotypic signatures that distinguish diseased from healthy states. These approaches can guide the discovery of targeted perturbations, including small-molecule drugs and genetic interventions, that modulate disease phenotypes toward healthier states. Here, we introduce PDGrapher, a causally inspired graph neural network (GNN) designed to predict combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem of directly predicting the perturbagens needed to achieve a desired response. PDGrapher is a GNN that embeds disease cell states into gene regulatory or protein-protein interaction networks, learns a latent representation of these states, and identifies the optimal combinatorial perturbations that most effectively shift the diseased state toward the desired treated state within that latent space. In experiments in nine cell lines with chemical perturbations, PDGrapher identified effective per-turbagens in up to 13.33% more test samples than competing methods and achieved a normalized discounted cumulative gain of up to 0.12 higher to classify therapeutic targets. It also demonstrated competitive performance on ten genetic perturbation datasets. A key advantage of PDGrapher is its direct prediction paradigm, in contrast to the indirect and computationally intensive models traditionally employed in phenotype-driven research. This approach accelerates training by up to 25 times compared to existing methods. PDGrapher provides a fast approach for identifying therapeutic perturbations and advancing phenotype-driven drug discovery.

This review outlines research on chemical biology using mainly microbial metabolites for agricultural applications. We established the RIKEN Natural Products Depository (NPDepo), housing many microbial metabolites, to support academic researchers who focus on drug discovery. We studied methods to stimulate secondary metabolism in microorganisms to collect various microbial products. The switch of secondary metabolism in microorganisms changes depending on the culture conditions. We discovered compounds that activate biosynthetic gene clusters in actinomycetes and filamentous fungi. Using these compounds, we succeeded in inducing the production of active compounds. Two approaches for screening bioactive compounds are described. One is phenotypic screening to explore antifungal compounds assisted by artificial intelligence (AI). AI can distinguish the morphological changes induced by antifungal compounds in filamentous fungi. The other is the chemical array method for detecting interactions between compounds and target proteins. Our chemical biology approach yielded many new compounds as fungicide candidates.

Tyrosine kinase inhibitors (TKIs) represent a pivotal class of targeted therapies in oncology, with multiple generations developed to address diverse molecular targets. Imatinib is the first TKI developed to target the BCR-ABL1 chimeric protein, which is the key driver oncogene implicated in Philadelphia chromosome-positive chronic myeloid leukemia (CML). Several second-generation tyrosine kinase inhibitors (2GTKIs), such as nilotinib, dasatinib, bosutinib, and radotinib (RTB), followed the groundbreaking introduction of imatinib. RTB occupies the unique position of being the least explored member of this class. While nilotinib, dasatinib, and bosutinib have garnered significant attention and extensive research focus, RTB remains relatively uncharted in comparison to its counterparts. Fundamental drug characteristics, such as the pharmacokinetic and pharmacodynamic properties of RTB, remain unavailable in existing sources. Compared to other 2GTKIs, RTB has been less utilized in combinatorial drug studies, and no investigations have been reported on its effects on solid tumors to date. However, the effects of RTB have been studied in acute myeloid leukemia (AML), multiple myeloma (MM), Parkinson's disease, and idiopathic pulmonary fibrosis (IPF). Although RTB has been investigated in some conditions, these studies are still in their preliminary stages and are comparatively lesser than studies on other 2GTKIs. This review is the first attempt that extensively presents a compilation of data on RTB and describes its therapeutic potential against blood and solid tumors. Further investigations on RTB could expand its chemotherapeutic usage in various solid tumors and enhance the possibility of drug repurposing in cancer therapy.

Repurposing drugs (DR) has become a viable approach to hasten the search for cures for neurodegenerative diseases (NDs). This review examines different off-target and on-target drug discovery techniques and how they might be used to find possible treatments for non-diagnostic depressions. Off-target strategies look at the known or unknown side effects of currently approved drugs for repositioning, whereas on-target strategies connect disease pathways to targets that can be treated with drugs. The review highlights the potential of experimental and computational methodologies, such as machine learning, proteomic techniques, network and genomics-based approaches, and in silico screening, in uncovering new drug-disease correlations. It also looks at difficulties and failed attempts at drug repurposing for NDs, highlighting the necessity of exact and standardised procedures to increase success rates. This review's objectives are to address the purpose of drug repurposing in human disorders, particularly neurological diseases, and to provide an overview of repurposing candidates that are presently undergoing clinical trials for neurological conditions, along with any possible causes and early findings. We then include a list of drug repurposing strategies, restrictions, and difficulties for upcoming research.

Continued efforts to discover new antibacterial molecules are critical to achieve a robust pre-clinical pipeline for new antibiotics. Screening of compound or natural product extract libraries remains a widespread approach and can benefit from the development of whole cell assays that are robust, simple and versatile, and allow for high throughput testing of antibacterial activity. In this study, we created and validated two bioluminescent reporter strains for high-throughput screening, one in Pseudomonas aeruginosa, and another in a hyperporinated and efflux-deficient Escherichia coli. We show that the bioluminescent strains have a large dynamic range that closely correlates with cell viability and is superior to conventional optical density (OD600) measurements, can detect dose-dependent antibacterial activity and be used for different drug discovery applications. We evaluated the assays' performance characteristics (signal to background ratio, signal window, Z' robust) and demonstrated their potential utility for antibiotic drug discovery in two examples. The P. aeruginosa bioluminescent reporter was used in a pilot screen of 960 repurposed compound libraries to identify adjuvants that potentiate the fluoroquinolone antibiotic ofloxacin. The E. coli bioluminescent reporter was used to test the antibacterial activity of bioactive bacterial supernatants and assist with bioassay-guided fractionation of the crude extracts.

Chemical proteomics enables the global analysis of small molecule-protein interactions in native biological systems and has emerged as a versatile approach for ligand discovery. The range of small molecules explored by chemical proteomics has, however, remained limited. Here, we describe a diversity-oriented synthesis (DOS)-inspired library of stereochemically defined compounds bearing diazirine and alkyne units for UV light-induced covalent modification and click chemistry enrichment of interacting proteins, respectively. We find that these "photo-stereoprobes" interact in a stereoselective manner with hundreds of proteins from various structural and functional classes in human cells and demonstrate that these interactions can form the basis for high-throughput screening-compatible NanoBRET assays. Integrated phenotypic screening and chemical proteomics identified photo-stereoprobes that modulate autophagy by engaging the mitochondrial serine protease CLPP. Our findings show the utility of DOS-inspired photo-stereoprobes for expanding the ligandable proteome, furnishing target engagement assays, and facilitating the discovery and characterization of bioactive compounds in phenotypic screens.

The emerging field of cancer phenomics provides comprehensive insights into tumor heterogeneity, promoting advances in personalized oncology. This study explores current research hotspots and future development trends in cancer phenomics through a bibliometric analysis of research from 2000 to 2023. Using data from the Web of Science Core Collection, we analyzed 1260 publications to identify global contributions and collaborative networks. Employing CiteSpace and VOSviewer tools, we examined research trends, highlighting disease progression, multi-omics integration, and phenotypic drug discovery as major focus areas. Key findings reveal that the United States, China, and the United Kingdom are leading contributors, with top institutions such as Harvard Medical School advancing research and fostering international collaboration. Additionally, the analysis underscores the prominence of double-positive (DP) T cells and natural killer (NK) cells in cancer immunology, showcasing their potential roles in phenotypic screening and cancer therapeutics. Despite advancements, the study notes ongoing challenges in translating phenomics research to clinical applications, suggesting that enhanced global partnerships and technological integration are essential. This analysis offers valuable perspectives for future research and highlights phenomics' transformative potential in precision oncology, advocating for its role in advancing cancer diagnosis, treatment, and prevention.

The technique of using naturally occurring light-emitting reactants (photoproteins and luciferases] that have been extracted from a wide range of animals is known as bioluminescence imaging, or BLI. This imaging offers important details on the location and functional state of regenerative cells inserted into various disease-modeling animals. Reports on gene expression patterns, cell motions, and even the actions of individual biomolecules in whole tissues and live animals have all been made possible by bioluminescence. Generally speaking, bioluminescent light in animals may be found down to a few centimetres, while the precise limit depends on the signal's brightness and the detector's sensitivity. We can now spatiotemporally visualize cell behaviors in any body region of a living animal in a time frame process, including proliferation, apoptosis, migration, and immunological responses, thanks to BLI. The biological applications of in vivo BLI in nondestructively monitoring biological processes in intact small animal models are reviewed in this work, along with some of the advancements that will make BLI a more versatile molecular imaging tool.

A series of 2,4-disubstituted-oxazole derivatives have been designed and synthesized based on compound 3a, a promising lead compound developed in our lab. Among these derivatives, the optimized compound 5k exhibited potent hypoglycemic activity, increasing glucose consumption by 60 % in HepG2 cells compared to the solvent control, and its activity was higher than that of metformin. Further investigation indicated that compound 5k exhibited negligible cytotoxic effects at a concentration of 25 μM in HepG2 and 3T3-L1 cells and showed moderate inhibitory activity against various subtypes of human cytochrome P450 subtypes. An oral glucose tolerance test confirmed that 5k is an effective hypoglycemic agent. Additionally, mechanistic studies suggested that 5k may exert its hypoglycemic activity through the activation of the AMPK pathway.

The rise of β-Lactamase mediated antibiotic resistance is a major concern for public health; hence, there is an urgent need to find new treatment approaches. Structure-guided drug repurposing offers a promising approach to swiftly deliver essential therapeutics in the fight against escalating antibiotic resistance. Here, a structure-guided virtual screening approach was used involving drug profiling, molecular docking, and molecular dynamics (MD) simulation to identify existing drugs against β-Lactamase-associated drug resistance. We exploited a large panel of FDA-approved drugs to an extensive in silico analysis to ascertain their ability to inhibit β-Lactamase. First, molecular docking investigations were performed to assess the binding affinities and interactions of screened molecules with the active site of β-Lactamase enzymes. Out of all the screened candidates, Aristospan was identified to possess promising characteristics, which include appropriate drug profiles, high binding specificity, and efficiency towards the binding pocket of β-Lactamase. Further analysis showed that Aristospan possesses several desirable biological characteristics and tends to bind to the β-Lactamase binding site. To explore the interactions further, the best docking pose of Aristospan was selected for MD simulations to assess the thermodynamic stability of the drug-enzyme complex and its conformational changes over 500 ns. The MD simulations in independent replica runs demonstrated that the β-Lactamase-Aristospan complex was stable in the 500 ns trajectory. These enlightening results suggest that Aristospan may harbor the potential for further evolution into a possible β-Lactamase inhibitor, with potential applications in overcoming antibiotic resistance in both Gram-positive and Gram-negative bacteria.

Clostridioides difficile, a gram-positive, toxin-producing, spore-forming anaerobe, is a major cause of antibiotic-associated diarrhoea. The bacterium's intrinsic drug resistance limits current treatment options to fidaxomicin and vancomycin for initial episodes, with anti-toxin B monoclonal antibody or faecal microbiota transplantation recommended for complicated or recurrent cases. This underscores the urgent need for novel therapeutics. In this study, we screened the MMV Pathogen Box at a 10 µM concentration against C. difficile R20291. Primary hits were evaluated for minimum inhibitory concentrations (MIC), killing kinetics, and biofilm inhibition. Bacterial cytological profiling (BCP) and transmission electron microscopy (TEM) were employed to study the mode of action. MMV676558 was further tested in a mouse model to assess survival, histopathology, and gut microbiota effects. We identified nineteen hits that inhibited over 50 % of C. difficile growth. MIC assays revealed three hits with MICs below 16 µg/mL: MMV676558, MMV688755, and MMV690027. These hits were effective against various C. difficile ribotypes. Killing kinetics were comparable or superior to vancomycin and fidaxomicin, and biofilm assays showed inhibitory effects. BCP and TEM analyses suggested membrane function disruption as the mode of action. Furthermore, MMV676558 demonstrated a protective effect in mice, with favourable histopathology and gut microbiota profiles. Given the urgent threat posed by C. difficile antibiotic resistance, discovering new treatments is a top priority. Our study identified three promising hits from the MMV Pathogen Box, with MMV676558 showing significant in vivo potential for further evaluation.

Cellular senescence is a natural barrier of the transition from premalignant cells to invasive cancer. Pharmacological induction of senescence has been proposed as a possible anticancer strategy. In this study, we found that deoxyarbutin inhibited the growth of glioblastoma (GBM) cells by inducing cellular senescence, independent of tyrosinase expression. Instead, deoxyarbutin induced mitochondrial oxidative stress and damage. These aberrant mitochondria were key to the p53-dependent senescence of GBM cells. Facilitating autophagy or mitigating mitochondrial oxidative stress both suppressed p53 expression and alleviated cellular senescence induced by deoxyarbutin. Thus, our study reveals that deoxyarbutin induces mitochondrial oxidative stress to trigger the p53-dependent senescence of GBM cells. Importantly, deoxyarbutin treatment resulted in accumulation of p53, induction of cellular senescence, and inhibition of tumor growth in a subcutaneous tumor model of mouse. In conclusion, our study reveals that deoxyarbutin has therapeutic potential for GBM by inducing mitochondrial oxidative stress for p53-dependent senescence of GBM cells.

Parkinson's disease (PD) is a neurodegenerative disorder marked by the loss of dopaminergic neurons in the substantia nigra. Despite progress, the pathogenesis remains unclear. Human midbrain organoids (hMLOs) have emerged as a promising model for studying PD, drug screening, and potential treatments. This review discusses the development of hMLOs, their application in PD research, and current challenges in organoid construction, highlighting possible optimization strategies.

Tumor-selective degradation of target proteins has the potential to offer superior therapeutic benefits with maximized therapeutic windows and minimized off-target effects. However, the development of effective lysosome-targeted degradation platforms for achieving selective protein degradation in tumors remains a substantial challenge. Cancer cells depend on certain solute carrier (SLC) transporters to acquire extracellular nutrients to sustain their metabolism and growth. This current study exploits facilitative glucose transporters (GLUTs), a group of SLC transporters widely overexpressed in numerous types of cancer, to drive the endocytosis and lysosomal degradation of target proteins in tumor cells. GLUT-targeting chimeras (GTACs) were generated by conjugating multiple glucose ligands to an antibody specific for the target protein. We demonstrate that the constructed GTACs can induce the internalization and lysosomal degradation of the extracellular and membrane proteins streptavidin, tumor necrosis factor-alpha (TNF-α), and human epidermal growth factor receptor 2 (HER2). Compared with the parent antibody, the GTAC exhibited higher potency in inhibiting the growth of tumor cells in vitro and enhanced tumor-targeting capacity in a tumor-bearing mouse model. Thus, the GTAC platform represents a novel degradation strategy that harnesses an SLC transporter for tumor-selective depletion of secreted and membrane proteins of interest.

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.