Traditional Chinese medicine with rich resources in China and definite therapeutic effects on complex diseases demonstrates great development potential. However, the complex composition, the unclear pharmacodynamic substances and mechanisms of action, and the lack of reasonable methods for evaluating clinical safety and efficacy have limited the research and development of innovative drugs based on traditional Chinese medicine. The progress in cutting-edge disciplines such as artificial intelligence and biomimetics, especially the emergence of cell painting and organ-on-a-chip, helps to identify and characterize the active ingredients in traditional Chinese medicine based on the changes in model characteristics, thus providing more accurate guidance for the development and application of traditional Chinese medicine. The application of phenotypic drug discovery in the research and development of innovative drugs based on traditional Chinese medicine is gaining increasing attention. In recent years, the technology for phenotypic drug discovery keeps advancing, which improves the early discovery rate of new drugs and the success rate of drug research and development. Accordingly, phenotypic drug discovery gradually becomes a key tool for the research on new drugs. This paper discusses the enormous potential of traditional Chinese medicine in the discovery and development of innovative drugs and illustrates how the application of phenotypic drug discovery, supported by cutting-edge technologies such as cell painting, deep learning, and organ-on-a-chip, propels traditional Chinese medicine into a new stage of development.

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.

Phenotypic drug discovery (PDD) is an effective drug discovery approach by observation of therapeutic effects on disease phenotypes, especially in complex disease systems. Triple-negative breast cancer (TNBC) is composed of several complex disease features, including high tumor heterogeneity, high invasive and metastatic potential, and a lack of effective therapeutic targets. Therefore, identifying effective and novel agents through PDD is a current trend in TNBC drug development. In this study, 23 novel small molecules were synthesized using 4-(phenylsulfonyl)morpholine as a pharmacophore. Among these derivatives, GL24 (4m) exhibited the lowest half-maximal inhibitory concentration value (0.90 µM) in MDA-MB-231 cells. To investigate the tumor-suppressive mechanisms of GL24, transcriptomic analyses were used to detect the perturbation for gene expression upon GL24 treatment. Followed by gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, multiple ER stress-dependent tumor suppressive signals were identified, such as unfolded protein response (UPR), p53 pathway, G2/M checkpoint, and E2F targets. Most of the identified pathways triggered by GL24 eventually led to cell-cycle arrest and then to apoptosis. In summary, we developed a novel 4-(phenylsulfonyl)morpholine derivative GL24 with a strong potential for inhibiting TNBC cell growth through ER stress-dependent tumor suppressive signals.

Among the strategies to overcome the underperformance of statins in cardiovascular diseases (CVDs), the development of drugs targeting the Proprotein Convertase Subtilisin-like Kexin type 9 (PCSK9) is considered one of the most promising. However, only anti-PCSK9 biological drugs have been approved to date, and orally available small-molecules for the treatment of hypercholesterolemic conditions are still missing on the market. In the present work, we describe the application of a phenotypic approach to the identification and optimization of 4-amino-2-pyridone derivatives as a new chemotype with anti-PCSK9 activity. Starting from an in-house collection of compounds, functional assays on HepG2 cells followed by a chemistry-driven hit optimization campaign, led to the potent anti-PCSK9 candidate 5c. This compound, at 5 μM, totally blocked PCSK9 secretion from HepG2 cells, significantly increased LDL receptor (LDLR) expression, and acted cooperatively with simvastatin by reducing its induction of PCSK9 expression. Finally, compound 5c also proved to be well tolerated in C57BL/6J mice at the tested concentration (40 mg/kg) with no sign of toxicity or behavior modifications.

Mycobacteria species include a large number of pathogenic organisms such as Mycobacterium tuberculosis, Mycobacterium leprae, and various non-tuberculous mycobacteria. Mycobacterial membrane protein large 3 (MmpL3) is an essential mycolic acid and lipid transporter required for growth and cell viability. In the last decade, numerous studies have characterized MmpL3 with respect to protein function, localization, regulation, and substrate/inhibitor interactions. This review summarizes new findings in the field and seeks to assess future areas of research in our rapidly expanding understanding of MmpL3 as a drug target. An atlas of known MmpL3 mutations that provide resistance to inhibitors is presented, which maps amino acid substitutions to specific structural domains of MmpL3. In addition, chemical features of distinct classes of Mmpl3 inhibitors are compared to provide insights into shared and unique features of varied MmpL3 inhibitors.

Lipid droplets (LDs), initially thought to be mere lipid storage structures, are highly dynamic organelles with complex functions that control cell fate and behavior. In recent years, their relevance as therapeutic targets for a wide array of human diseases has been well established. Consequently, efforts to develop tools to study them have intensified, including assays that can accurately track LD levels in clinically relevant cell-based models. We previously reported that LD accumulation destines podocytes for lipotoxicity and cell death in renal diseases of metabolic and nonmetabolic origin. We also showed that LD accumulation in those cells serves as both a marker for disease progression and as a therapeutic target. Here, we describe a robust phenotypic screening method, using differentiated human podocytes, for identifying small-molecule compounds that rescue podocytes from LD accumulation and lipotoxicity under cellular stress. Major assay advances include 1) the use of a solvatochromic dye to improve LD staining, reduce background noise, and improve detection accuracy, 2) use of confocal imaging to reduce vertical overlap of LDs and enable accurate counting, 3) combining membrane and cytoskeleton stains to improve cell segmentation in confocal mode, and 4) use of an optimized spot detection algorithm that requires minimal configuration per individual run. The assay is robust and yields a Z-factor that is consistently >0.5.

Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal \'hit\' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).

Stem cell transplantation and genetic therapies offer potential cures for patients with sickle cell disease (SCD), but these options require advanced medical facilities and are expensive. Consequently, these treatments will not be available for many years to the majority of patients suffering from this disease. What is urgently needed now is an inexpensive oral drug in addition to hydroxyurea, the only drug approved by the FDA that inhibits sickle-hemoglobin polymerization. Here, we report the results of the first phase of our phenotypic screen of the 12,657 compounds of the Scripps ReFRAME drug repurposing library using a recently developed high-throughput assay to measure sickling times following deoxygenation to 0% oxygen of red cells from sickle trait individuals. The ReFRAME library is a very important collection because the compounds are either FDA-approved drugs or have been tested in clinical trials. From dose-response measurements, 106 of the 12,657 compounds exhibit statistically significant antisickling at concentrations ranging from 31 nM to 10 μM. Compounds that inhibit sickling of trait cells are also effective with SCD cells. As many as 21 of the 106 antisickling compounds emerge as potential drugs. This estimate is based on a comparison of inhibitory concentrations with free concentrations of oral drugs in human serum. Moreover, the expected therapeutic potential for each level of inhibition can be predicted from measurements of sickling times for cells from individuals with sickle syndromes of varying severity. Our results should motivate others to develop one or more of these 106 compounds into drugs for treating SCD.

Natural products represent an excellent source of unprecedented anticancer compounds. However, the identification of the mechanism of action remains a major challenge. Several techniques and methodologies have been considered, but with limited success. In this work, we explored the combination of live cell imaging and machine learning techniques as a promising tool to depict in a fast and affordable test the mode of action of natural compounds with antiproliferative activity. To develop the model, we selected the non-small cell lung cancer cell line SW1573, which was exposed to the known antimitotic drugs paclitaxel, colchicine and vinblastine. The novelty of our methodology focuses on two main features with the highest relevance, (a) meaningful phenotypic metrics, and (b) fast Fourier transform (FFT) of the time series of the phenotypic parameters into their corresponding amplitudes and phases. The resulting algorithm was able to cluster the microtubule disruptors, and meanwhile showed a negative correlation between paclitaxel and the other treatments. The FFT approach was able to group the samples as efficiently as checking by eye. This methodology could easily scale to group a large amount of data without visual supervision.