An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Autocorrelation analysis of a phenotypic screen reveals hidden drug activity.

Phenotype based screening is a powerful tool to evaluate cellular drug response. Through high content fluorescence imaging of simple fluorescent labels and complex image analysis phenotypic measurements can identify subtle compound-induced cellular changes unique to compound mechanisms of action (MoA). Recently, a screen of 1008 compounds in three cell lines was reported where analysis detected changes in cellular phenotypes and accurately identified compound MoA for roughly half the compounds. However, we were surprised that DNA alkylating agents and other compounds known to induce or impact the DNA damage response produced no measured activity in cells with fluorescently labeled 53BP1-a canonical DNA damage marker. We hypothesized that phenotype analysis is not sensitive enough to detect small changes in 53BP1 distribution and analyzed the screen images with autocorrelation image analysis. We found that autocorrelation analysis, which quantifies fluorescently-labeled protein clustering, identified higher compound activity for compounds and MoAs known to impact the DNA damage response, suggesting altered 53BP1 recruitment to damaged DNA sites. We then performed experiments under more ideal imaging settings and found autocorrelation analysis to be a robust measure of changes to 53BP1 clustering in the DNA damage response. These results demonstrate the capacity of autocorrelation to detect otherwise undetectable compound activity and suggest that autocorrelation analysis of specific proteins could serve as a powerful screening tool.

Fission Yeast as a Platform for Antibacterial Drug Screens Targeting Bacterial Cytoskeleton Proteins.

Bacterial cytoskeletal proteins such as FtsZ and MreB perform essential functions such as cell division and cell shape maintenance. Further, FtsZ and MreB have emerged as important targets for novel antimicrobial discovery. Several assays have been developed to identify compounds targeting nucleotide binding and polymerization of these cytoskeletal proteins, primarily focused on FtsZ. Moreover, many of the assays are either laborious or cost-intensive, and ascertaining whether these proteins are the cellular target of the drug often requires multiple methods. Finally, the toxicity of the drugs to eukaryotic cells also poses a problem. Here, we describe a single-step cell-based assay to discover novel molecules targeting bacterial cytoskeleton and minimize hits that might be potentially toxic to eukaryotic cells. Fission yeast is amenable to high-throughput screens based on microscopy, and a visual screen can easily identify any molecule that alters the polymerization of FtsZ or MreB. Our assay utilizes the standard 96-well plate and relies on the ability of the bacterial cytoskeletal proteins to polymerize in a eukaryotic cell such as the fission yeast. While the protocols described here are for fission yeast and utilize FtsZ from Staphylococcus aureus and MreB from Escherichia coli, they are easily adaptable to other bacterial cytoskeletal proteins that readily assemble into polymers in any eukaryotic expression hosts. The method described here should help facilitate further discovery of novel antimicrobials targeting bacterial cytoskeletal proteins.

Exploiting urine-derived induced pluripotent stem cells for advancing precision medicine in cell therapy, disease modeling, and drug testing.

The field of regenerative medicine has witnessed remarkable advancements with the emergence of induced pluripotent stem cells (iPSCs) derived from a variety of sources. Among these, urine-derived induced pluripotent stem cells (u-iPSCs) have garnered substantial attention due to their non-invasive and patient-friendly acquisition method. This review manuscript delves into the potential and application of u-iPSCs in advancing precision medicine, particularly in the realms of drug testing, disease modeling, and cell therapy. U-iPSCs are generated through the reprogramming of somatic cells found in urine samples, offering a unique and renewable source of patient-specific pluripotent cells. Their utility in drug testing has revolutionized the pharmaceutical industry by providing personalized platforms for drug screening, toxicity assessment, and efficacy evaluation. The availability of u-iPSCs with diverse genetic backgrounds facilitates the development of tailored therapeutic approaches, minimizing adverse effects and optimizing treatment outcomes. Furthermore, u-iPSCs have demonstrated remarkable efficacy in disease modeling, allowing researchers to recapitulate patient-specific pathologies in vitro. This not only enhances our understanding of disease mechanisms but also serves as a valuable tool for drug discovery and development. In addition, u-iPSC-based disease models offer a platform for studying rare and genetically complex diseases, often underserved by traditional research methods. The versatility of u-iPSCs extends to cell therapy applications, where they hold immense promise for regenerative medicine. Their potential to differentiate into various cell types, including neurons, cardiomyocytes, and hepatocytes, enables the development of patient-specific cell replacement therapies. This personalized approach can revolutionize the treatment of degenerative diseases, organ failure, and tissue damage by minimizing immune rejection and optimizing therapeutic outcomes. However, several challenges and considerations, such as standardization of reprogramming protocols, genomic stability, and scalability, must be addressed to fully exploit u-iPSCs' potential in precision medicine. In conclusion, this review underscores the transformative impact of u-iPSCs on advancing precision medicine and highlights the future prospects and challenges in harnessing this innovative technology for improved healthcare outcomes.

AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening.

Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.

Engineered platforms for mimicking cardiac development and drug screening.

Congenital heart defects are associated with significant health challenges, demanding a deep understanding of the underlying biological mechanisms and, thus, better devices or platforms that can recapitulate human cardiac development. The discovery of human pluripotent stem cells has substantially reduced the dependence on animal models. Recent advances in stem cell biology, genetic editing, omics, microfluidics, and sensor technologies have further enabled remarkable progress in the development of in vitro platforms with increased fidelity and efficiency. In this review, we provide an overview of advancements in in vitro cardiac development platforms, with a particular focus on technological innovation. We categorize these platforms into four areas: two-dimensional solid substrate cultures, engineered substrate architectures that enhance cellular functions, cardiac organoids, and embryos/explants-on-chip models. We conclude by addressing current limitations and presenting future perspectives.

Identification of a potent PCNA-p15-interaction inhibitor by autodisplay-based peptide library screening.

Proliferating cell nuclear antigen (PCNA) is an essential factor for DNA metabolism. The influence of PCNA on DNA replication and repair, combined with the high expression rate of PCNA in various tumours renders PCNA a promising target for cancer therapy. In this context, an autodisplay-based screening method was developed to identify peptidic PCNA interaction inhibitors. A 12-mer randomized peptide library consisting of 2.54 × 106 colony-forming units was constructed and displayed at the surface of Escherichia coli BL21 (DE3) cells by autodisplay. Cells exhibiting an enhanced binding to fluorescent mScarlet-I-PCNA were enriched in four sorting rounds by flow cytometry. This led to the discovery of five peptide variants with affinity to mScarlet-I-PCNA. Among these, P3 (TCPLRWITHDHP) exhibited the highest binding signal. Subsequent flow cytometric analysis revealed a dissociation constant of 0.62 µM for PCNA-P3 interaction. Furthermore, the inhibition of PCNA interactions was investigated using p15, a PIP-box containing protein involved in DNA replication and repair. P3 inhibited the PCNA-p1551-70 interaction with a half maximal inhibitory activity of 16.2 µM, characterizing P3 as a potent inhibitor of the PCNA-p15 interaction.

Proposal for considerations during human iPSC-derived cardiac organoid generation for cardiotoxicity drug testing.

Human iPSC-derived cardiac organoids (hiPSC-COs) for cardiotoxicity drug testing via the variety of cell lines and unestablished protocols may lead to differences in response results due to a lack of criteria for generation period and size. To ensure reliable drug testing, it is important for researchers to set optimal generation period and size of COs according to the cell line and protocol applied in their studies. Hence, we sought to propose a process to establish minimum criteria for the generation duration and size of hiPSC-COs for cardiotoxic drug testing. We generated hiPSC-COs of different sizes based on our protocol and continuously monitored organoids until they indicated a minimal beating rate change as a control that could lead to more accurate beating rate changes on drug testing. Calcium transients and physiological tests to assess the functionality of hiPSC-COs on selected generation period, which showed regular cardiac beating, and immunostaining assays to compare characteristics were performed. We explained the generation period and size that exhibited and maintained regular beating rate changes on hiPSC-COs, and lead to reliable response results to cardiotoxicity drugs. We anticipate that this study will offer valuable insights into considering the appropriate generation period and size of hiPSC-COs ensuring reliable outcomes in cardiotoxicity drug testing.

Review: 3D cell models for organ-on-a-chip applications.

Two-dimensional (2D) cultures do not fully reflect the human organs' physiology and the real effectiveness of the used therapy. Therefore, three-dimensional (3D) models are increasingly used in bioanalytical science. Organ-on-a-chip systems are used to obtain cellular in vitro models, better reflecting the human body's in vivo characteristics and allowing us to obtain more reliable results than standard preclinical models. Such 3D models can be used to understand the behavior of tissues/organs in response to selected biophysical and biochemical factors, pathological conditions (the mechanisms of their formation), drug screening, or inter-organ interactions. This review characterizes 3D models obtained in microfluidic systems. These include spheroids/aggregates, hydrogel cultures, multilayers, organoids, or cultures on biomaterials. Next, the methods of formation of different 3D cultures in Organ-on-a-chip systems are presented, and examples of such Organ-on-a-chip systems are discussed. Finally, current applications of 3D cell-on-a-chip systems and future perspectives are covered.

C. elegans: a prominent platform for modeling and drug screening in neurological disorders.

INTRODUCTION: Human neurodevelopmental and neurodegenerative diseases (NDevDs and NDegDs, respectively) encompass a broad spectrum of disorders affecting the nervous system with an increasing incidence. In this context, the nematode C. elegans, has emerged as a benchmark model for biological research, especially in the field of neuroscience. AREAS COVERED: The authors highlight the numerous advantages of this tiny worm as a model for exploring nervous system pathologies and as a platform for drug discovery. There is a particular focus given to describing the existing models of C. elegans for the study of NDevDs and NDegDs. Specifically, the authors underscore their strong applicability in preclinical drug development. Furthermore, they place particular emphasis on detailing the common techniques employed to explore the nervous system in both healthy and diseased states. EXPERT OPINION: Drug discovery constitutes a long and expensive process. The incorporation of invertebrate models, such as C. elegans, stands as an exemplary strategy for mitigating costs and expediting timelines. The utilization of C. elegans as a platform to replicate nervous system pathologies and conduct high-throughput automated assays in the initial phases of drug discovery is pivotal for rendering therapeutic options more attainable and cost-effective.

Bronchioalveolar organoids: A preclinical tool to screen toxicity associated with antibody-drug conjugates.

Despite extensive preclinical testing, cancer therapeutics can result in unanticipated toxicity to non-tumor tissue in patients. These toxicities may pass undetected in preclinical experiments due to modeling limitations involving poor biomimicry of 2-dimensional in vitro cell cultures and due to lack of interspecies translatability in in vivo studies. Instead, primary cells can be grown into miniature 3-dimensional structures that recapitulate morphological and functional aspects of native tissue, termed "organoids." Here, human bronchioalveolar organoids grown from primary alveolar epithelial cells were employed to model lung epithelium and investigate off-target toxicities associated with antibody-drug conjugates (ADCs). ADCs with three different linker-payload combinations (mafodotin, vedotin, and deruxtecan) were tested in bronchioalveolar organoids generated from human, rat, and nonhuman primate lung cells. Organoids demonstrated antibody uptake and changes in viability in response to ADC exposure that model in vivo drug sensitivity. RNA sequencing identified inflammatory activation in bronchioalveolar cells in response to deruxtecan. Future studies will explore specific cell populations involved in interstitial lung disease and incorporate immune cells to the culture.

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

Prostate Cancer Organoids for Tumor Modeling and Drug Screening.

Prostate cancer (PCa) is the second most common malignancy and the fifth leading cause of cancer death in men worldwide. Despite its prevalence, the highly heterogenic PCa has shown difficulty to establish representative cell lines that reflect the diverse phenotypes and different stages of the disease in vitro and hence hard to model in preclinical research. The patient-derived organoid (PDO) technique has emerged as a groundbreaking three-dimensional (3D) tumor modeling platform in cancer research. This versatile assay relies on the unique ability of cancer stem cells (CSCs) to self-organize and differentiate into organ-like mini structures. The PDO culture system allows for the long-term maintenance of cancer cells derived from patient tumor tissues. Moreover, it recapitulates the parental tumor features and serves as a superior preclinical model for in vitro tumor representation and personalized drug screening. Henceforth, PDOs hold great promise in precision medicine for cancer. Herein, we describe the detailed protocol to establish and propagate organoids derived from isolated cell suspensions of PCa patient tissues or cell lines using the 3D semisolid Matrigel™-based hanging-drop method. In addition, we highlight the relevance of PDOs as a tool for evaluating drug efficacy and predicting tumor response in PCa patients.

Spheroid Drug Sensitivity Screening in Glioma Stem Cell Lines.

In vitro drug sensitivity screens are important tools in the discovery of anti-cancer drug combination therapies. Typically, these in vitro drug screens are performed on cells grown in a monolayer. However, these two-dimensional (2D) models are considered less accurate compared to three-dimensional (3D) spheroid cell models; this is especially true for glioma stem cell lines. Cells grown in spheres activate different signaling pathways and are considered more representative of in vivo models than monolayer cell lines. This protocol describes a method for in vitro drug screening of spheroid lines; mouse and human glioma stem cell lines are used as an example. This protocol describes a 3D spheroid drug sensitivity and synergy assay that can be used to determine if a drug or drug combination induces cell death and if two drugs synergize. Glioma stem cell lines are modified to express RFP. Cells are plated in low attachment round well bottom 96 plates, and spheres are allowed to form overnight. Drugs are added, and the growth is monitored by measuring the RFP signal over time using the Incucyte live imaging system, a fluorescence microscope embedded in the tissue culture incubator. Half maximal inhibitory concentration (IC50), median lethal dose (LD50), and synergy score are subsequently calculated to evaluate sensitivities to drugs alone or in combination. The three-dimensional nature of this assay provides a more accurate reflection of tumor growth, behavior, and drug sensitivities in vivo, thus forming the basis for further preclinical investigation.

Protocol for high throughput 3D drug screening of patient derived melanoma and renal cell carcinoma.

High Throughput Screening (HTS) with 3D cell models is possible thanks to the recent progress and development in 3D cell culture technologies. Results from multiple studies have demonstrated different drug responses between 2D and 3D cell culture. It is now widely accepted that 3D cell models more accurately represent the physiologic conditions of tumors over 2D cell models. However, there is still a need for more accurate tests that are scalable and better imitate the complex conditions in living tissues. Here, we describe ultrahigh throughput 3D methods of drug response profiling in patient derived primary tumors including melanoma as well as renal cell carcinoma that were tested against the NCI oncologic set of FDA approved drugs. We also tested their autologous patient derived cancer associated fibroblasts, varied the in-vitro conditions using matrix vs matrix free methods and completed this in both 3D vs 2D rendered cancer cells. The result indicates a heterologous response to the drugs based on their genetic background, but not on their maintenance condition. Here, we present the methods and supporting results of the HTS efforts using these 3D of organoids derived from patients. This demonstrated the possibility of using patient derived 3D cells for HTS and expands on our screening capabilities for testing other types of cancer using clinically approved anti-cancer agents to find drugs for potential off label use.

Standardization of zebrafish drug testing parameters for muscle diseases.

Skeletal muscular diseases predominantly affect skeletal and cardiac muscle, resulting in muscle weakness, impaired respiratory function and decreased lifespan. These harmful outcomes lead to poor health-related quality of life and carry a high healthcare economic burden. The absence of promising treatments and new therapies for muscular disorders requires new methods for candidate drug identification and advancement in animal models. Consequently, the rapid screening of drug compounds in an animal model that mimics features of human muscle disease is warranted. Zebrafish are a versatile model in preclinical studies that support developmental biology and drug discovery programs for novel chemical entities and repurposing of established drugs. Due to several advantages, there is an increasing number of applications of the zebrafish model for high-throughput drug screening for human disorders and developmental studies. Consequently, standardization of key drug screening parameters, such as animal husbandry protocols, drug compound administration and outcome measures, is paramount for the continued advancement of the model and field. Here, we seek to summarize and explore critical drug treatment and drug screening parameters in the zebrafish-based modeling of human muscle diseases. Through improved standardization and harmonization of drug screening parameters and protocols, we aim to promote more effective drug discovery programs.

Consideration of vendor-related differences in hepatic metabolic stability data to optimize early ADME screening in drug discovery.

Hepatic metabolic stability is a crucial determinant of oral bioavailability and plasma concentrations of a compound, and its measurement is important in early drug discovery. Preliminary metabolic stability estimations are commonly performed in liver microsomal fractions. At the National Center for Advancing Translational Sciences, a single-point assay in rat liver microsomes (RLM) is employed for initial stability assessment (Tier I) and a multi-point detailed stability assay is employed as a Tier II assay for promising compounds. Although the in vitro and in vivo metabolic stability of compounds typically exhibit good correlation, conflicting results may arise in certain cases. While investigating one such instance, we serendipitously found vendor-related RLM differences in metabolic stability and metabolite formation, which had implications for in vitro and in vivo correlations. In this study, we highlight the importance of considering vendor differences in hepatic metabolic stability data and discuss strategies to avoid these pitfalls.

Cardiovascular human organ-on-a-chip platform for disease modeling, drug development, and personalized therapy.

Cardiovascular organ-on-a-chip (OoC) devices are composed of engineered or native functional tissues that are cultured under controlled microenvironments inside microchips. These systems employ microfabrication and tissue engineering techniques to recapitulate human physiology. This review focuses on human OoC systems to model cardiovascular diseases, to perform drug screening, and to advance personalized medicine. We also address the challenges in the generation of organ chips that can revolutionize the large-scale application of these systems for drug development and personalized therapy.

FLECS technology for high-throughput screening of hypercontractile cellular phenotypes in fibrosis: A function-first approach to anti-fibrotic drug discovery.

The pivotal role of myofibroblast contractility in the pathophysiology of fibrosis is widely recognized, yet HTS approaches are not available to quantify this critically important function in drug discovery. We developed, validated, and scaled-up a HTS platform that quantifies contractile function of primary human lung myofibroblasts upon treatment with pro-fibrotic TGF-ß1. With the fully automated assay we screened a library of 40,000 novel small molecules in under 80 h of total assay run-time. We identified 42 hit compounds that inhibited the TGF-ß1-induced contractile phenotype of myofibroblasts, and enriched for 19 that specifically target myofibroblasts but not phenotypically related smooth muscle cells. Selected hits were validated in an ex vivo lung tissue models for their inhibitory effects on fibrotic gene upregulation by TGF-ß1. Our results demonstrate that integrating a functional contraction test into the drug screening process is key to identify compounds with targeted and diverse activity as potential anti-fibrotic agents.