Biology subjects

Kuzikov, M.

Publications and source records attributed to Kuzikov, M..

6 recordsLinked to original sources

bioRxiv2025

Morphological cell profiling for drug repurposing against SARS-CoV-2 infection

Antiviral drug discovery has traditionally focused on targeting viral proteins, while host-directed strategies remain largely underexplored. Here, present a systematic drug repurposing strategy leveraging morphological profiling to identify host-targeting antivirals. Our image-based approach combines viral protein immunostaining with high-content Cell Painting analysis to simultaneously assess viral replication and provide in-depth analysis of host cell responses. By screening 5,275 repurposable drugs against SARS-CoV-2 infected cells, we identified compounds that reversed the infected cell phenotype, including ones not detected by conventional cytopathicity and antibody-based assays. A counter-screen excluded compounds whose antiviral activity was likely driven by drug-induced phospholipidosis (DIPL). Pathway enrichment analysis of compounds validated by both Cell Painting dose-response and DIPL assays, revealed host processes frequently hijacked by viruses, including innate immune responses and kinases. Among the top hits, both novel candidates, such as serdemetan, and previously reported broad-spectrum antivirals, such as sunitinib, were identified. Our approach constitutes an adaptable and scalable platform suited for diverse viral pathogens and cell systems. We provide a resource of open access screening data, images, and automated analysis pipelines to advance both antiviral discovery and pandemic preparedness.

microbiology↗

bioRxiv2025

Interdisciplinary Study on Drug-Induced-Phospholipidosis of Repurposing Libraries through Machine Learning and Experimental Evaluation in Different Cell Lines

Bigger PicturePhospholipidosis is a cellular condition characterized by the excessive accumulation of phospholipids within cells, that also can be induced by medications--especially those known as cationic amphiphilic drugs. While this phenomenon is not always harmful in itself, it can interfere with how cells respond in laboratory experiments and complicate the interpretation of drug screening results, leading to potential delays or failures in the development of new therapies. As the pharmaceutical industry increasingly relies on high-throughput screening and machine learning to accelerate early-stage drug discovery, there is growing interest in identifying compounds that may cause phospholipidosis before they move too far through the development pipeline. In response to this challenge, we at the EU-Horizon HLTH Remedi4ALL project compiled and analyzed one of the most comprehensive datasets to date--over 5,000 repurposed drugs tested across multiple cell lines--to better understand which compounds are likely to induce phospholipidosis. Using this dataset, we developed a machine learning model capable of predicting the risk of phospholipidosis based on a drugs chemical structure. By validating our approach across diverse experimental settings, our goal is to provide the broader scientific and pharmaceutical communities with a practical tool to flag problematic compounds early, ultimately helping bring safer, more effective treatments to patients faster. Highlights- A comprehensive dataset for a repurposing collection, comprising 5,228 compounds, for induction of phospholipidosis in A549-ACE2 and Vero-E6 cells -A machine learning model developed using in-house experimental phospholipidosis data across multiple cell lines -Exploration of chemical features that induce phospholipidosis and investigation of the relationships underlying different cell line susceptibilities -A case study: correlation of drug-induced phospholipidosis with antiviral compound effects against SARS-CoV-2 using phenotypic screening data Phospholipidosis (PLD), a cellular adverse effect that is, among others, caused by numerous cationic amphiphilic drugs. Interest is raised within pharma discovery to predict this phenomenon, as it can impact the outcome of phenotypic cellular screens and significantly delay drug development processes. The development of accurate and validated machine learning models for predicting drug-induced PLD across different cell lines and research centers could provide a valuable early application tool for the pharmaceutical industry, potentially accelerating drug discovery and reducing the risk of late-stage failures. We report here the assembly, curation, testing and modeling of one of the largest datasets of repurposed drugs (5K+) tested for PLD induction on different cell lines. A machine-learning classification method was developed and validated to predict whether molecules are prone to induce PLD effects when applied in cell-based screens. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/663638v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@1c4760org.highwire.dtl.DTLVardef@92084dorg.highwire.dtl.DTLVardef@15effbaorg.highwire.dtl.DTLVardef@1e73f4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

molecular biology↗

bioRxiv2025

Optimization and evaluation of complementary degrader discovery assays for application in screening

Targeted protein degradation (TPD) mediated by molecular glues is an innovative pharmaceutical paradigm. By binding to and modulating the surface of an E3-ligase component, molecular glue degraders can facilitate the recruitment of a specific target protein (or vice versa) and, ultimately, invoke target degradation. This mode of action results in specific challenges for the development of rational discovery strategies, and complex hit validation workflows may be required to reliably eliminate compounds that elicit non-specific effects. With the aim to guide screening efforts, we optimized two orthogonal cell-based, target-centric assays for degrader discovery: (1) a time-resolved FRET assay directly quantifying levels of target protein and its degradation (signal inhibition), and (2) an assay coupling TPD to cell growth (signal rescue). To enable a deeper understanding of the individual assays strengths and limitations, we compared their statistical performance as well as respective hit populations by screening a specifically designed collection of about 1000 compounds containing well annotated reference compounds and known frequent hitters. We found that the signal rescue format reliably and specifically captured active target degraders while it efficiently filtered out interfering or frequent hitter compounds. Importantly, this format achieved to retrieve lower potency hits, which might be desirable in order to confidently include as many diverse chemical starting points as possible at the start of a drug discovery project.

pharmacology and toxicology↗

bioRxiv2023

Resolving the pharmacological redox-sensitivity of SARS-CoV-2 PLpro in drug repurposing screening enabled identification of the competitive GRL-0617 binding site inhibitor CPI-169

The SARS CoV-2 Papain-Like protease has multiple roles in the viral replication cycle, related to both its polypeptide cleavage function and its capacity to antagonize host immune response. Targeting PLpro function is recognized as a promising mechanism to modulate viral replication whilst supporting host immune responses. However, development of PLpro specific inhibitors remains challenging. Upcoming studies revealed the limitation of reported inhibitors by profiling them through a pipeline of enzymatic, binding and cellular activity assays showing unspecific activity. GRL-0617 remained the only validated molecule with demonstrated anti-viral activity in cells. In this study we refer to the pitfalls of redox-sensitivity of PLpro. Using a screening-based approach to identify inhibitors of PLpro proteolytic activity, we made extensive efforts to validate the active compounds over a range of conditions and readouts, emphasising the need for comprehensive orthogonal data when profiling putative PLpro inhibitors. The remaining active compound CPI-169, showed to compete with GRL-0617 in NMR-based experiments, suggesting to share a similar binding mode, opening novel design opportunities for further developments as antiviral agents. Author summaryThe increasing knowledge about SARS-CoV-2 allowed the development of multiple strategies to contain the spread of COVID-19 infection. Nevertheless, effective antiviral pharmacological treatments are still rare and viral evolution allowed a fast adaptation and escape from available containment methods. The papain like protease (PLpro) has now become the next most promising SARS-CoV-2 therapeutic due to its multiple functions in virus replication cycle and antagonization of host immune response. However, due to inherent flexibility and sensitivity of this enzyme specific inhibitors are rare. Here we report on a screening strategy using repurposing of known drugs that takes into account PLpro characteristics to identify new inhibitors, showing the success of the approach by identifying CPI-169 that competitive targets the well described GRL-0617 inhibitor binding pocket of PLpro and helping to design further antiviral agents.

molecular biology↗

bioRxiv2020

Identification of inhibitors of SARS-CoV-2 3CL-Pro enzymatic activity using a small molecule in-vitro repurposing screen

Compound repurposing is an important strategy for the identification of effective treatment options against SARS-CoV-2 infection and COVID-19 disease. In this regard, SARS-CoV-2 main protease (3CL-Pro), also termed M-Pro, is an attractive drug target as it plays a central role in viral replication by processing the viral polyproteins pp1a and pp1ab at multiple distinct cleavage sites. We here report the results of a repurposing program involving 8.7 K compounds containing marketed drugs, clinical and preclinical candidates, and small molecules regarded as safe in humans. We confirmed previously reported inhibitors of 3CL-Pro, and have identified 62 additional compounds with IC50 values below 1 M and profiled their selectivity towards Chymotrypsin and 3CL-Pro from the MERS virus. A subset of 8 inhibitors showed anti-cytopathic effect in a Vero-E6 cell line and the compounds thioguanosine and MG-132 were analysed for their predicted binding characteristics to SARS-CoV-2 3CL-Pro. The X-ray crystal structure of the complex of myricetin and SARS-Cov-2 3CL-Pro was solved at a resolution of 1.77 [A], showing that myricetin is covalently bound to the catalytic Cys145 and therefore inhibiting its enzymatic activity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/422677v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@17ca2aeorg.highwire.dtl.DTLVardef@19c5159org.highwire.dtl.DTLVardef@1a0adf6org.highwire.dtl.DTLVardef@1fd05cd_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOAbstract Figure.C_FLOATNO Workflow for identification and profiling of inhibitors of SARS-CoV-2 3CL-Pro using a large scale repurposing and bioactive compound collection (rhs). Primary assay principle based on quenched FRET peptide substrate of SARS-CoV-2 3CL-Pro (lhs). Inhibiting compounds reduce fluorescence signal relative to DMSO controls. Hit profiling using X-ray. C_FIG

molecular biology↗

bioRxiv2020

A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unfortunately, unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ~30,000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ~200 virtual screenings of compound libraries on selected protein structures, we redefine the proteins druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

biophysics↗