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.

3D imaging of SARS-CoV-2 infected hamster lungs by X-ray phase contrast tomography enables drug testing.

X-ray Phase Contrast Tomography (XPCT) based on wavefield propagation has been established as a high resolution three-dimensional (3D) imaging modality, suitable to reconstruct the intricate structure of soft tissues, and the corresponding pathological alterations. However, for biomedical research, more is needed than 3D visualisation and rendering of the cytoarchitecture in a few selected cases. First, the throughput needs to be increased to cover a statistically relevant number of samples. Second, the cytoarchitecture has to be quantified in terms of morphometric parameters, independent of visual impression. Third, dimensionality reduction and classification are required for identification of effects and interpretation of results. To address these challenges, we here design and implement a novel integrated and high throughput XPCT imaging and analysis workflow for 3D histology, pathohistology and drug testing. Our approach uses semi-automated data acquisition, reconstruction and statistical quantification. We demonstrate its capability for the example of lung pathohistology in Covid-19. Using a small animal model, different Covid-19 drug candidates are administered after infection and tested in view of restoration of the physiological cytoarchitecture, specifically the alveolar morphology. To this end, we then use morphometric parameter determination followed by a dimensionality reduction and classification based on optimal transport. This approach allows efficient discrimination between physiological and pathological lung structure, thereby providing quantitative insights into the pathological progression and partial recovery due to drug treatment. Finally, we stress that the XPCT image chain implemented here only used synchrotron radiation for validation, while the data used for analysis was recorded with laboratory µ CT radiation, more easily accessible for pre-clinical research.

Screening of Small-Molecule Libraries Using SARS-CoV-2-Derived Sequences Identifies Novel Furin Inhibitors.

SARS-CoV-2 is the pathogen responsible for the most recent global pandemic, which has claimed hundreds of thousands of victims worldwide. Despite remarkable efforts to develop an effective vaccine, concerns have been raised about the actual protection against novel variants. Thus, researchers are eager to identify alternative strategies to fight against this pathogen. Like other opportunistic entities, a key step in the SARS-CoV-2 lifecycle is the maturation of the envelope glycoprotein at the RARR685↓ motif by the cellular enzyme Furin. Inhibition of this cleavage greatly affects viral propagation, thus representing an ideal drug target to contain infection. Importantly, no Furin-escape variants have ever been detected, suggesting that the pathogen cannot replace this protease by any means. Here, we designed a novel fluorogenic SARS-CoV-2-derived substrate to screen commercially available and custom-made libraries of small molecules for the identification of new Furin inhibitors. We found that a peptide substrate mimicking the cleavage site of the envelope glycoprotein of the Omicron variant (QTQTKSHRRAR-AMC) is a superior tool for screening Furin activity when compared to the commercially available Pyr-RTKR-AMC substrate. Using this setting, we identified promising novel compounds able to modulate Furin activity in vitro and suitable for interfering with SARS-CoV-2 maturation. In particular, we showed that 3-((5-((5-bromothiophen-2-yl)methylene)-4-oxo-4,5 dihydrothiazol-2-yl)(3-chloro-4-methylphenyl)amino)propanoic acid (P3, IC50 = 35 µM) may represent an attractive chemical scaffold for the development of more effective antiviral drugs via a mechanism of action that possibly implies the targeting of Furin secondary sites (exosites) rather than its canonical catalytic pocket. Overall, a SARS-CoV-2-derived peptide was investigated as a new substrate for in vitro high-throughput screening (HTS) of Furin inhibitors and allowed the identification of compound P3 as a promising hit with an innovative chemical scaffold. Given the key role of Furin in infection and the lack of any Food and Drug Administration (FDA)-approved Furin inhibitor, P3 represents an interesting antiviral candidate.

Pseudovirus-Based Systems for Screening Natural Antiviral Agents: A Comprehensive Review.

Since the outbreak of COVID-19, researchers have been working tirelessly to discover effective ways to combat coronavirus infection. The use of computational drug repurposing methods and molecular docking has been instrumental in identifying compounds that have the potential to disrupt the binding between the spike glycoprotein of SARS-CoV-2 and human ACE2 (hACE2). Moreover, the pseudovirus approach has emerged as a robust technique for investigating the mechanism of virus attachment to cellular receptors and for screening targeted small molecule drugs. Pseudoviruses are viral particles containing envelope proteins, which mediate the virus's entry with the same efficiency as that of live viruses but lacking pathogenic genes. Therefore, they represent a safe alternative to screen potential drugs inhibiting viral entry, especially for highly pathogenic enveloped viruses. In this review, we have compiled a list of antiviral plant extracts and natural products that have been extensively studied against enveloped emerging and re-emerging viruses by pseudovirus technology. The review is organized into three parts: (1) construction of pseudoviruses based on different packaging systems and applications; (2) knowledge of emerging and re-emerging viruses; (3) natural products active against pseudovirus-mediated entry. One of the most crucial stages in the life cycle of a virus is its penetration into host cells. Therefore, the discovery of viral entry inhibitors represents a promising therapeutic option in fighting against emerging viruses.

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.

Nonclinical Safety Testing of RNA Vaccines.

In this chapter, we will first consider the overall goal of nonclinical safety testing during drug development and have a brief overview of its regulatory background. We will then discuss some basic requirements of safety/toxicity testing before concentrating on the safety testing of RNA vaccines and developing a sample RNA vaccine safety testing program.

Integrated high-throughput drug screening microfluidic system for comprehensive ocular toxicity assessment.

Traditional experimental methodologies suffer from a few limitations in the toxicological evaluation of the preservatives added to eye drops. In this study, we overcame these limitations by using a microfluidic device. We developed a microfluidic system featuring a gradient concentration generator for preservative dosage control with microvalves and micropumps, automatically regulated by a programmable Arduino board. This system facilitated the simultaneous toxicological evaluation of human corneal epithelial cells against eight different concentrations of preservatives, allowing for quadruplicate experiments in a single run. In our study, the IC50 values for healthy eyes and those affected with dry eyes syndrome showed an approximately twofold difference. This variation is likely attributable to the duration for which the preservative remained in contact with corneal cells before being washed off by the medium, suggesting the significance of exposure time in the cytotoxic effect of preservatives. Our microfluidic system, automated by Arduino, simulated healthy and dry eye environments to study benzalkonium chloride toxicity and revealed significant differences in cell viability, with IC50 values of 0.0033% for healthy eyes and 0.0017% for dry eyes. In summary, we implemented the pinch-to-zoom feature of an electronic tablet in our microfluidic system, offering innovative alternatives for eye research.

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.

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.

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.

A simple and rapid pre-clinical in vivo model reveals comparative cardiotoxicity profiles of kinase inhibitors.

Despite significant success, targeted therapeutics such as kinase inhibitors (KIs) still pose adverse events such as the cardiotoxicity. There is a lot of variation in the type and intensity of cardiotoxicity caused by different KIs and current pre-clinical models are inadequate to predict it. Thus, there is a need to develop more simple and rapid models for screening of novel KIs at the pre-clinical step itself. We thus aimed to establish a rapid and robust pre-clinical animal model for predicting cardiotoxicity of KIs and identify comparative cardiotoxicity profiles of a panel of FDA-approved KIs. Heart rate measurement and survival analysis of Daphnia was performed at regular intervals following treatment with ten KIs that were approved for the treatment of various cancers. The heart rates of Daphnia as well as the survival varied between KIs in a dose and time dependent manner suggesting differential cardiotoxicity profiles of various KIs. Further, the correlation between the cardiotoxicity and survival also varied among the ten KIs. Importantly, sorafenib and vemurafenib displayed maximum and least cardiotoxicity, respectively. The comparative cardiotoxicity profiles also are in conformity with the previous studies indicating the utility of Daphnia as a valuable and relevant animal model to rapidly predict the cardiotoxicity of novel KIs at a pre-clinical stage.

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening.

Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.

A dual-target SPR screening system for simultaneous ligand discovery of SARS-CoV-2 spike protein and its receptor ACE2 from Chinese herbs.

Traditional Chinese Medicine (TCM) is a supremely valuable resource for the development of drug discovery. Few methods are capable of hunting for potential molecule ligands from TCM towards more than one single protein target. In this study, a novel dual-target surface plasmon resonance (SPR) biosensor was developed to perform targeted compound screening of two key proteins involved in the cellular invasion process of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): the spike (S) protein receptor binding domain (RBD) and the angiotensin-converting enzyme 2 (ACE2). The screening and identification of active compounds from six Chinese herbs were conducted taking into consideration the multi-component and multi-target nature of Traditional Chinese Medicine (TCM). Puerarin from Radix Puerariae Lobatae was discovered to exhibit specific binding affinity to both S protein RBD and ACE2. The results highlight the efficiency of the dual-target SPR system in drug screening and provide a novel approach for exploring the targeted mechanisms of active components from Chinese herbs for disease treatment.

Construction and characterization of recombinant senecavirus A expressing secreted luciferase for antiviral screening.

Senecavirus A (SVA) is a newly identified picornavirus associated with swine vesicular disease and neonatal mortality. The development of an SVA incorporating an exogenous reporter gene provides a powerful tool for viral research. In this study, we successfully constructed a recombinant SVA expressing Gaussia Luciferase (Gluc), termed rSVA-Gluc. The growth kinetics of rSVA-Gluc in BHK-21 cells were found to be comparable to those of the parental virus, and Gluc activity paralleled the virus growth curve. Genetic analysis revealed stable inheritance of the inserted reporter protein genes for at least six generations. We evaluated the utility of rSVA-Gluc in antiviral drug screening, and the results highlighted its potential as an effective tool for such purposes against SVA. DATA AVAILABILITY STATEMENT: The data that support the findings of this study are available on request from the corresponding author.

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.