Drug Development Research Articles

Model-Informed Clinical Development of Once-Every-6-Month Injection of Paliperidone Palmitate in Patients with Schizophrenia: A Pharmacometric Bridging Approach (Part I).

A model-informed drug development (MIDD) approach was implemented for paliperidone palmitate (PP) 6-month (PP6M) clinical development, using pharmacokinetics and pharmacokinetic/pharmacodynamic model-based simulations. PP6M pharmacokinetics were simulated by extending the PP 3-month (PP3M) pharmacokinetic model to account for increased injection volume, and hence dose. Contribution of the MIDD approach to the design of the pivotal PP6M phase-3 study (PP6M/PP3M noninferiority study, NCT03345342) investigating schizophrenia relapse rates was twofold: (1) PP6M dose selection, and (2) hypothesis generation that lower trough concentrations (Ctrough) associated with PP6M, relative to PP3M, were not associated with lower efficacy, which was to be evaluated in the phase-3 study. Moreover, accompanied by an intense sampling scheme to adequately characterize paliperidone pharmacokinetics and to elucidate the potential relationship between concentration and safety/efficacy, the bridging strategy eliminated the need for additional phase-1/phase-2 clinical studies. Using a MIDD bridging strategy, PP6M doses were selected that, compared with PP3M, were expected to have a similar range of exposures and a noninferior relapse rate and safety profile. Clinical data from PP6M/PP3M noninferiority study confirmed that PP6M, compared with PP3M, had a similar range of exposures (T'jollyn etal. in Eur J Drug Metab Pharmacokinet 2024), as well as a noninferior relapse rate and safety profile (this manuscript). Consistency of the MIDD approach with observed clinical outcomes confirmed the hypothesis that lower Ctrough did not lead to increased relapse rates at the doses administered. Although higher paliperidone peak concentrations are achieved with corresponding doses of PP6M relative to PP3M in the phase-3 clinical study, types and incidences of treatment-related adverse events were comparable between PP6M and PP3M groups and no new safety concerns emerged for PP6M (Najarian etal. in Int J Neuropsychopharmacol 25(3):238-251, 2022).

In vitro antioxidant and anticancer potential of intra-cellular ethyl acetate extract of marine-derived fungus Talaromyces tratensis SS10

Marine fungi are well-known for producing structurally distinct secondary metabolites, making them potential sources of novel therapies. The present investigation aims to study the in vitro antioxidant and anticancer potential of intra-cellular crude ethyl acetate extracts of Talaromyces tratensis SS10. In the present study, qualitative and quantitative phytochemical studies of various solvent extracts of T. tratensis have been carried out using standard protocols. Later, ethyl acetate extract of T. tratensis was analyzed for phytochemicals using Gas Chromatography Mass Spectrometry (GC-MS). Further, the antioxidant properties of the T. tratensis ethyl acetate extract have been done by Ferric reducing antioxidant power assay (FRAP). Further, the anticancer potential of this extract has been carried out by MTT assay against human cancer cells such as MDA MB 231, HeLa, and HT-29. Ethyl acetate exhibited a higher yield of chemical extraction than the other solvents used. The GCMS analysis of T. tratensis ethyl acetate extract revealed major phytoconstituents such as N-(1,1-Dimethylpropyl)-2,2,3-trimethylaziridine-1-carboxamide, 1-Undecanol, 5,5 Dimethyl-3-vinyl cyclohex-2-en-1-one, 1,2-Benzenedicarboxylic acid, bis (2-methyl propyl) ester. T. tratensis ethyl acetate extract showed the highest percentage of Fe3+ reduction (48.093±1.469%) at 120 μg/mL, with an IC50 value of 157.26 μg/mL concentration. Furthermore, 100 μg/mL of the extract showed significant cell death rates in cytotoxic assays, indicating a low percentage of viable cells for all three examined cell lines. The T. tratensis ethyl acetate extract has shown a dose-dependent cytotoxic effect against all tested cancer cell lines. The better IC50 value (6.25 μg/mL) was recorded in the case of HeLa cell lines followed by 12.5 μg/mL for both MDA MB 231 and HT-29 cell lines. The presence of bioactive compounds such as Benzeneethanamine, N-[(pentafluorophenyl)methylene]-beta.,3,4-tris[(trimethylsilyl) oxy]-, 1,2-Benzenedicarboxylic acid, bis(2-methylpropyl) ester, and cyclononasiloxane, octadecamethyl- may have contributed to the ethyl acetate extracts’ strong antioxidant and anticancer properties. The current study’s findings show that T. tratensis SS10 has the potential for drug development due to its chemical constituents, which possess various biological activities.

Predicting Drug Resistance in Gastric Cancer with Mutation in the Human Epidermal Growth Factor Receptor 2 (HER2) And Machine Learning Technique

Gastric cancer with mutations in the human epidermal growth factor receptor (HER2) can be regarded as one of the leading causes of cancer mortality in the world. Targeted tyrosine kinase inhibitors (TKIs) developed against HER2 yielded optimistic results in improving patients' survival rates and life quality. Nevertheless, drug resistance can influence the critical supportive documents of treatment plans and decrease the treatment effectiveness after about one year. Predicting the efficacy of HER2-TKI drugs or therapies for patients with HER2-mutated gastric cancer is a critical research field. In the present study, a personalized drug response prediction model based on molecular dynamics simulations and machine learning is presented to predict response to first-generation drugs approved by the Ministry of Health in patients with gastric cancer. In the molecular dynamics simulation, the patient's mutation status is considered. The patient's unique mutation status was modeled using molecular dynamics simulations to extract geometric features at the molecular level.Furthermore, additional clinical features are incorporated into the machine learning model to predict drug response. The complete features encompass demographic and clinical characteristics, geometrical properties of the drug-target binding site, and binding free energy (RBFE) of the drug-target complex from molecular dynamics simulations. Drug response prediction utilizes the XGBoost classifier, which achieved leading-edge performance for a 4-level drug response prediction task (PDRP) with 97.5% accuracy, 93% sensitivity, 96.5% specificity, and 94% F1 score. The present research has demonstrated that modeling the binding cavity geometry, in tandem with the binding free energy, can effectively predict drug response. Interestingly, the clinical information, while significant, did not significantly influence the model's performance. This exciting finding opens up new avenues for testing the proposed model on various types of cancers, potentially revolutionizing drug development strategies. DOI: https://doi.org/10.52783/pst.391

Guiding organs-on-chips towards applications: a balancing act between integration of advanced technologies and standardization

Organs-on-chips (OoC) are in vitro models that emulate key functionalities of tissues or organs in a miniaturized and highly controlled manner. Due to their high versatility, OoC have evolved as promising alternatives to animal testing for a more effective drug development pipeline. Additionally, OoC are revealing increased predictive power for toxicity screening applications as well as (patho-) physiology research models. It is anticipated that enabling technologies such as biofabrication, multimodality imaging, and artificial intelligence will play a critical role in the development of the next generation of OoC. These domains are expected to increase the mimicry of the human micro-physiology and functionality, enhance screening of cellular events, and generate high-content data for improved prediction. Although exponentially growing, the OoC field will strongly benefit from standardized tools to upgrade its implementational power. The complexity derived from the integration of multiple technologies and the current absence of concrete guidelines for establishing standards may be the reason for the slower adoption of OoC by industry, despite the fast progress of the field. Therefore, we argue that it is essential to consider standardization early on when using new enabling technologies, and we provide examples to illustrate how to maintain a focus on technology standards as these new technologies are used to build innovative OoC applications. Moreover, we stress the importance of informed design, use, and analysis decisions. Finally, we argue that this early focus on standards in innovation for OoC will facilitate their implementation.

Bioguided Fractionation of Phyllanthus spp.: Unveiling Anticancer Potential through Metabolomic Correlation and ADMETox Insights.

Cancer remains a significant global health concern, with mortality rates steadily rising and prompting an urgent search for effective treatments. This study focuses on the medicinal properties of plants from the Phyllanthus genus, specifically Phyllanthus amarus and Phyllanthus niruri, which have shown promise in traditional medicine. Through bioguided fractionation using preparative high-performance liquid chromatography (HPLC), bioactive compounds were isolated and identified using ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-QTOF-MSE) and nuclear magnetic resonance (NMR) spectroscopy. Chemometric analyses such as principal component analysis (PCA) aided in understanding metabolite distribution. Biological assays demonstrated cytotoxic activities of specific fractions against cancer cell lines, notably the PhyN 4n fraction from P. niruri, which induced S-phase cell cycle arrest and apoptosis in HL60 cells. These findings underscore the anticancer potential of Phyllanthus species and lay the groundwork for future drug development efforts. The study's integration of advanced analytical techniques, chemometrics, and biological assays provides valuable insights for harnessing natural products in the fight against cancer.

Case Report book review: Computer-aided drug development

Case Report book review: Computer-aided drug development

Lipidization as a Tool for Peptide Drug Development

Peptide therapeutics are becoming increasingly important in the quest for effective compounds to treat various difficult-to-cure diseases. As potential therapeutics, peptides have many favorable chemical and pharmacological properties, ranging from their great diversity to their high affinity to various types of natural receptors. However, despite these and other advantages, they also have their pitfalls, such as a very limited stability in the organism. They have a short half-life and tend to be excreted from the body very quickly. In order to achieve a better pharmacological effect, it is desirable to find new means of modifying peptides to enable them to be used as effective drugs. There are many ways of altering the peptide structure. In this review, we summarize the approaches currently used to modify the peptide constitution with a focus on lipidization. Simultaneously this review summarizes all lipidized peptide-based therapeutics that are currently on the market.

A Minimal PBPK/PD Model with Expansion-Enhanced Target-Mediated Drug Disposition to Support a First-in-Human Clinical Study Design for a FLT3L-Fc Molecule.

FLT3L-Fc is a half-life extended, effectorless Fc-fusion of the native human FLT3-ligand. In cynomolgus monkeys, treatment with FLT3L-Fc leads to a complex pharmacokinetic/pharmacodynamic (PK/PD) relationship, with observed nonlinear PK and expansion of different immune cell types across different dose levels. A minimal physiologically based PK/PD model with expansion-enhanced target-mediated drug disposition (TMDD) was developed to integrate the molecule's mechanism of action, as well as the complex preclinical and clinical PK/PD data, to support the preclinical-to-clinical translation of FLT3L-Fc. In addition to the preclinical PK data of FLT3L-Fc in cynomolgus monkeys, clinical PK and PD data from other FLT3-agonist molecules (GS-3583 and CDX-301) were used to inform the model and project the expansion profiles of conventional DC1s (cDC1s) and total DCs in peripheral blood. This work constitutes an essential part of our model-informed drug development (MIDD) strategy for clinical development of FLT3L-Fc by projecting PK/PD in healthy volunteers, determining the first-in-human (FIH) dose, and informing the efficacious dose in clinical settings. Model-generated results were incorporated in regulatory filings to support the rationale for the FIH dose selection.

Bioprospecting of Aspergillus sp. as a promising repository for anti-cancer agents: a comprehensive bibliometric investigation

Cancer remains a significant global health challenge, claiming nearly 10 million lives in 2020 according to the World Health Organization. In the quest for novel treatments, fungi, especially Aspergillus species, have emerged as a valuable source of bioactive compounds with promising anticancer properties. This study conducts a comprehensive bibliometric analysis to map the research landscape of Aspergillus in oncology, examining publications from 1982 to the present. We observed a marked increase in research activity starting in 2000, with a notable peak from 2005 onwards. The analysis identifies key contributors, including Mohamed GG, who has authored 15 papers with 322 citations, and El-Sayed Asa, with 14 papers and 264 citations. Leading countries in this research field include India, Egypt, and China, with King Saud University and Cairo University as the leading institutions. Prominent research themes identified are “endophyte,” “green synthesis,” “antimicrobial,” “anti-cancer,” and “biological activities,” indicating a shift towards environmentally sustainable drug development. Our findings highlight the considerable potential of Aspergillus for developing new anticancer therapies and underscore the necessity for further research to harness these natural compounds for clinical use.

Biomarkers for Precision Patient Selection in Cancer Therapy Approvals in the US, from 2011 to 2023.

During the period of 2011-2023, the US Food and Drug Administration (US FDA) granted 139 accelerated and 329 regular approvals for 86 and 152 cancer therapeutic products, respectively. The percentage of approvals for a biomarker-defined population was numerically higher in accelerated approvals in comparison to regular approvals, that is, 48% vs. 40%. From 2011-2016 to 2017-2023, there was an increasing number of approvals with biomarker-defined populations in lung and breast cancers, serving as the primary driver for the overall increase in the percentage of approvals for biomarker-defined populations in solid tumors relative to hematological malignancies. Over the years, approvals were incorporating a more diverse collection of distinct biomarkers, from 3 in 2011 to 16 in 2022. Overall, HER2, hormone receptor (HR), EGFR, ALK, BRAF, and PD-L1-defined populations received the highest numbers of approvals. The FDA decision on approving a biomarker-defined or an all-comers population may depend on a number of factors and may evolve over time based on emerging evidence. The review discusses selected FDA approvals where a pivotal trial enrolled an all-comers population but the approved indication was restricted to a biomarker-defined population, as well as challenges in clinical trial design in the context of precision medicine. The prominent role of biomarkers in optimizing trial design and identifying a population most likely to benefit from treatment underlines the significance of a comprehensive understanding of disease biology and drug mechanisms. Our review illustrates that biomarker-driven approaches enhance the likelihood of identifying optimal patient populations, potentially streamlining trials through accelerated approval pathways for cancer drug development.

Additive Cytotoxic and Colony-Formation Inhibitory Effects of Aspirin and Metformin on PI3KCA-Mutant Colorectal Cancer Cells.

Human malignancies are one of the major health-related issues throughout the world and are anticipated to rise in the future. Despite huge investments made in anticancer drug development, limited success has been obtained and the average number of FDA approvals per year is declining. So, an increasing interest in drug repurposing exists. Metformin (MET) and aspirin (ASP) possess anticancer properties. This work aims to test the effect of these two drugs in combination on colorectal cancer (CRC) cells in vitro. The effects of MET and/or ASP on cell proliferation, viability, migratory ability, anchorage-independent growth ability (colony formation), and nutrient uptake were determined in two (HT-29 and Caco-2) human CRC cell lines. Individually, MET and ASP possessed antiproliferative, cytotoxic, and antimigratory effects and reduced colony formation in HT-29 cells (BRAF- and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α (PI3KCA)-mutant), although MET did not affect either 3H-deoxy-D-glucose or 14C-butyrate uptake and lactate production, and ASP caused only a small decrease in 14C-butyrate uptake. Moreover, in these cells, the combination of MET and ASP resulted in a tendency to an increase in the cytotoxic effect and in a potentiation of the inhibitory effect on colony formation, although no additive antiproliferative and antimigratory effects, and no effect on nutrient uptake and lactate production were observed. In contrast, MET and ASP, both individually and in combination, were almost devoid of effects on Caco-2 cells (BRAF- and PI3KCA-wild type). We suggest that inhibition of PI3K is the common mechanism involved in the anti-CRC effect of both MET, ASP and their combination and, therefore, that the combination of MET + ASP may especially benefit PI3KCA-mutant CRC cases, which currently have a poor prognostic.

Historical Perspective and Current Trends in Anticancer Drug Development.

Cancer is considered one of the leading causes of death in the 21st century. The intensive search for new anticancer drugs has been actively pursued by chemists and pharmacologists for decades, focusing either on the isolation of compounds with cytotoxic properties from plants or on screening thousands of synthetic molecules. Compounds that could potentially become candidates for new anticancer drugs must have the ability to inhibit proliferation and/or induce apoptosis in cancer cells without causing too much damage to normal cells. Some anticancer compounds were discovered by accident, others as a result of long-term research. In this review, we have presented a brief history of the development of the most important groups of anticancer drugs, pointing to the fact that they all have many side effects.

Synergistic induction of blood–brain barrier properties

Blood-brain barrier (BBB) models derived from human stem cells are powerful tools to improve our understanding of cerebrovascular diseases and to facilitate drug development for the human brain. Yet providing stem cell-derived endothelial cells with the right signaling cues to acquire BBB characteristics while also retaining their vascular identity remains challenging. Here, we show that the simultaneous activation of cyclic AMP and Wnt/β-catenin signaling and inhibition of the TGF-β pathway in endothelial cells robustly induce BBB properties in vitro. To target this interaction, we present a small-molecule cocktail named cARLA, which synergistically enhances barrier tightness in a range of BBB models across species. Mechanistically, we reveal that the three pathways converge on Wnt/β-catenin signaling to mediate the effect of cARLA via the tight junction protein claudin-5. We demonstrate that cARLA shifts the gene expressional profile of human stem cell-derived endothelial cells toward the in vivo brain endothelial signature, with a higher glycocalyx density and efflux pump activity, lower rates of endocytosis, and a characteristic endothelial response to proinflammatory cytokines. Finally, we illustrate how cARLA can improve the predictive value of human BBB models regarding the brain penetration of drugs and targeted nanoparticles. Due to its synergistic effect, high reproducibility, and ease of use, cARLA has the potential to advance drug development for the human brain by improving BBB models across laboratories.

Deep profiling of potential substrate atlas of porcine epidemic diarrhea virus 3C-like protease.

Coronavirus (CoV) 3C-like protease (3CLpro) is essential for viral replication and is involved in immune escape by proteolyzing host proteins. Deep profiling the 3CLpro substrates in the host proteome extends our understanding of viral pathogenesis and facilitates antiviral drug discovery. Here, 3CLpro from porcine epidemic diarrhea virus (PEDV), an enteropathogenic CoV, was used as a model which to identify the potential 3CLpro cleavage motifs in all porcine proteins. We characterized the selectivity of PEDV 3CLpro at sites P5-P4'. We then compiled the 3CLpro substrate preferences into a position-specific scoring matrix and developed a 3CLpro profiling strategy to delineate the protein substrate landscape of CoV 3CLpro. We identified 1,398 potential targets in the porcine proteome containing at least one putative cleavage site and experimentally validated the reliability of the substrate degradome. The PEDV 3CLpro-targeted pathways are involved in mRNA processing, translation, and key effectors of autophagy and the immune system. We also demonstrated that PEDV 3CLpro suppresses the type 1 interferon (IFN-I) cascade via the proteolysis of multiple signaling adaptors in the retinoic acid-inducible gene I (RIG-I) signaling pathway. Our composite method is reproducible and accurate, with an unprecedented depth of coverage for substrate motifs. The 3CLpro substrate degradome establishes a comprehensive substrate atlas that will accelerate the investigation of CoV pathogenicity and the development of anti-CoV drugs.IMPORTANCECoronaviruses (CoVs) are major pathogens that infect humans and animals. The 3C-like protease (3CLpro) encoded by CoV not only cleaves the CoV polyproteins but also degrades host proteins and is considered an attractive target for the development of anti-CoV drugs. However, the comprehensive characterization of an atlas of CoV 3CLpro substrates is a long-standing challenge. Using porcine epidemic diarrhea virus (PEDV) 3CLpro as a model, we developed a method that accurately predicts the substrates of 3CLpro and comprehensively maps the substrate degradome of PEDV 3CLpro. Interestingly, we found that 3CLpro may simultaneously degrade multiple molecules responsible for a specific function. For instance, it cleaves at least four adaptors in the RIG-I signaling pathway to suppress type 1 interferon production. These findings highlight the complexity of the 3CLpro substrate degradome and provide new insights to facilitate the development of anti-CoV drugs.

Late-stage diversification of bacterial natural products through biocatalysis.

Bacterial natural products (BNPs) are very important sources of leads for drug development and chemical novelty. The possibility to perform late-stage diversification of BNPs using biocatalysis is an attractive alternative route other than total chemical synthesis or metal complexation reactions. Although biocatalysis is gaining popularity as a green chemistry methodology, a vast majority of orphan sequenced genomic data related to metabolic pathways for BNP biosynthesis and its tailoring enzymes are underexplored. In this review, we report a systematic overview of biotransformations of 21 molecules, which include derivatization by halogenation, esterification, reduction, oxidation, alkylation and nitration reactions, as well as degradation products as their sub-derivatives. These BNPs were grouped based on their biological activities into antibacterial (5), antifungal (5), anticancer (5), immunosuppressive (2) and quorum sensing modulating (4) compounds. This study summarized 73 derivatives and 16 degradation sub-derivatives originating from 12 BNPs. The highest number of biocatalytic reactions was observed for drugs that are already in clinical use: 28 reactions for the antibacterial drug vancomycin, followed by 18 reactions reported for the immunosuppressive drug rapamycin. The most common biocatalysts include oxidoreductases, transferases, lipases, isomerases and haloperoxidases. This review highlights biocatalytic routes for the late-stage diversification reactions of BNPs, which potentially help to recognize the structural optimizations of bioactive scaffolds for the generation of new biomolecules, eventually leading to drug development.

Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography

Objective. Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems. Approach. Firstly, Taylor expansion framework is introduced to reduce time and computational cost. Secondly, group sparsity based on structural prior is introduced to improve reconstruction accuracy. Thirdly, alternating iterative solution based on accelerated gradient descent algorithm is introduced to solve the problem. Main results. Numerical simulation and in vivo experimental results demonstrate that, in comparison to existing methods, the proposed approach significantly enhances reconstruction speed without a degradation of quality, particularly when confronted with background fluorescence interference from other organs. Significance. Our research greatly reduces time and computational cost, providing strong support for real-time monitoring of liver metabolism.

AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

Biomarkers as Endpoints in Clinical Trials for Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disease that imposes economic and societal burden. Biomarkers have played a crucial role in the recent approval of aducanumab and lecanemab as disease-modifying therapies which marked a significant milestone for the treatment of AD. The inclusion of biomarkers in AD trials facilitates precise diagnosis, monitors safety, demonstrates target engagement, and supports disease modification. This study analyzed the utilization state and trends of biomarkers as endpoints in AD trials. In this retrospective study, trials were collected by searching clinicaltrials.gov using the term "Alzheimer". Primary and secondary outcomes were analyzed separately for each phase. Among the 1,048 analyzed trials, 313 (29.87%) adopted biomarkers as primary endpoints and 364 (34.73%) as secondary endpoints, mainly in phases 1 and 2. The top three biomarkers adopted as primary endpoints in phases 1, 2, and 3 were amyloid-PET, tau-PET, and MRI. The top three biomarkers adopted as secondary endpoints, in phase 1, were cerebrospinal fluid (CSF) amyloid-β (Aβ), blood Aβ and amyloid-PET; in phase 2, they were MRI, CSF Aβ, and CSF phospho-tau; and in phase 3, they were amyloid PET, MRI, and blood Aβ. There was a statistically significant increase in the adoption of biomarkers as primary endpoints in phase 2 trials (p = 0.001) and secondary endpoints in phase 3 trials (p = 0.001). The growing recognition of the importance of biomarkers in AD trial' design and drug development is evident by the significant steady increase in biomarkers' utilization in phases 2 and 3.

Optimization of quenched fluorescent peptide substrates of SARS-CoV-2 3CLpro main protease (Mpro) from proteomic identification of P6-P6' active site specificity.

SARS-CoV-2 3C-like main protease (3CLpro) is essential for protein excision from the viral polyprotein. 3CLpro inhibitor drug development to block SARS-CoV-2 replication focuses on the catalytic non-prime (P) side for specificity and potency, but the importance of the prime (P') side in substrate specificity and for drug development remains underappreciated. We determined the P6-P6' specificity for 3CLpro from >800 cleavage sites that we identified using Proteomic Identification of Cleavage site Specificity (PICS). Cleavage occurred after the canonical P1-Gln and non-canonical P1-His and P1-Met residues. Moreover, P3 showed a preference for Arg/Lys and P3' for His. Essential H-bonds between the N-terminal Ser1 of protomer-B in 3CLpro dimers form with P1-His, but not with P1-Met. Nonetheless, cleavage occurs at P1-Met456 in native MAP4K5. Elevated reactive oxygen species in SARS-CoV-2 infection oxidize methionines. Molecular simulations revealed P1-MetOX forms an H-bond with Ser1 and notably, strong positive cooperativity between P1-Met with P3'-His was revealed, which enhanced peptide-cleavage rates. The highly plastic S3' subsite accommodates P3'-His that displays stabilizing backbone H-bonds with Thr25 lying central in a "'threonine trio" (Thr24-Thr25-Thr26) in the P'-binding domain I. Molecular docking simulations unveiled structure-activity relationships impacting 3CLpro-substrate interactions, and the role of these structural determinants was confirmed by MALDI-TOF-MS cleavage assays of P1'- and P3'-positional scanning peptide libraries carrying a 2nd optimal cut-site as an internal positive control. These data informed the design of two new and highly soluble 3CLproquenched-fluorescent peptide substrates for improved FRET monitoring of 3CLpro activity with 15× improved sensitivity over current assays.IMPORTANCEFrom global proteomics identification of >800 cleavage sites, we characterized the P6-P6' active site specificity of SARS-CoV-2 3CLpro using proteome-derived peptide library screens, molecular modeling simulations, and focussed positional peptide libraries. In P1', we show that alanine and serine are cleaved 3× faster than glycine and the hydrophobic small amino acids Leu, Ile, or Val prevent cleavage of otherwise optimal non-prime sequences. In characterizing non-canonical non-prime P1 specificity, we explored the unusual P1-Met specificity, discovering enhanced cleavage when in the oxidized state (P1-MetOX). We unveiled unexpected amino acid cooperativity at P1-Met with P3'-His and noncanonical P1-His with P2-Phe, and the importance of the threonine trio (Thr24-Thr25-Thr26) in the prime side binding domain I in defining prime side binding in SARS-CoV-2 3CLpro. From these analyses, we rationally designed quenched-fluorescence natural amino acid peptide substrates with >15× improved sensitivity and high peptide solubility, facilitating handling and application for screening of new antiviral drugs.

Antifungal activity of β-lapachone against a fluconazole-resistant Candida auris strain.

Candida spp. can be found in the human microbiome. However, immunocompromised patients are likely to develop invasive Candida infections, with mortality rates higher than 50%. The discovery of C. auris, a species that rapidly acquire antifungal resistance, increased the concern about Candida infections. The limited number of antifungal agents and the high incidence of resistance to them make imperative the development of new antifungal drugs. β-lapachone is a biological active naphthoquinone that displays antifungal activity against C. albicans and C. glabrata. The aim of this study was to evaluate if this substance affects C. auris growth and elucidate its mechanism of action. A fluconazole-resistant C. auris isolate was used in this study. The antifungal activity of β-lapachone was determined through microbroth dilution assays, and its mechanism of action was evaluated using fluorescent probes. Interaction with fluconazole and amphotericin B was assessed by disk diffusion assay and checkerboard. β-lapachone inhibited planktonic C. auris cell growth by 92.7%, biofilm formation by 84.9%, and decrease the metabolism of preformed biofilms by 87.1% at 100µg/ml. At 100µg/ml, reductions of 30% and 59% of Calcofluor White and Nile red fluorescences were observed, indicating that β-lapachone affects cell wall chitin and neutral lipids content, respectively. Also, the ratio 590nm/529nm of JC-1 decreased 52%, showing that the compound affects mitochondria. No synergism was observed between β-lapachone and fluconazole or amphotericin B. Data show that β-lapachone may be a promising candidate to be used as monotherapy to treat C. auris resistant infections.