Early Drug Research Articles

Challenges and Advances in Biomarker Detection for Rapid and Accurate Sepsis Diagnosis: An Electrochemical Approach.

Sepsis is a life-threatening condition with high mortality rates due to delayed treatment of patients. The conventional methodology for blood diagnosis takes several hours, which suspends treatment, limits early drug administration, and affects the patient's recovery. Thus, rapid, accurate, bedside (onsite), economical, and reliable sepsis biomarker reading of the clinical sample is an emergent need for patient lifesaving. Electrochemical label-free biosensors are specific and rapid devices that are able to perform analysis at the patient's bedside; thus, they are considered an attractive methodology in a clinical setting. To reveal their full diagnostic potential, electrode architecture strategies of fabrication are highly desirable, particularly those able to preserve specific antibody-antigen attraction, restrict non-specific adsorption, and exhibit high sensitivity with a low detection limit for a target biomarker. The aim of this review is to provide state-of-the-art methodologies allowing the fabrication of ultrasensitive and highly selective electrochemical sensors for sepsis biomarkers. This review focuses on different methods of label-free biomarker sensors and discusses their advantages and disadvantages. Then, it highlights effective ways of avoiding false results and the role of molecular labels and functionalization. Recent literature on electrode materials and antibody grafting strategies is discussed, and the most efficient methodology for overcoming the non-specific attraction issues is listed. Finally, we discuss the existing electrode architecture for specific biomarker readers and promising tactics for achieving quick and low detection limits for sepsis biomarkers.

Colloidal Aggregation Confounds Cell-Based Covid-19 Antiviral Screens.

Colloidal aggregation is one of the largest contributors to false positives in early drug discovery. Here, we consider aggregation's role in cell-based infectivity assays in Covid-19 drug repurposing. We investigated the potential aggregation of 41 drug candidates reported as SARs-CoV-2 entry inhibitors. Of these, 17 formed colloidal particles by dynamic light scattering and exhibited detergent-dependent enzyme inhibition. To evaluate the impact of aggregation on antiviral efficacy in cells, we presaturated the colloidal drug suspensions with BSA or spun them down by centrifugation and measured the effects on spike pseudovirus infectivity. Antiviral potencies diminished by at least 10-fold following both BSA and centrifugation treatments, supporting a colloid-based mechanism. Aggregates induced puncta of the labeled spike protein in fluorescence microscopy, consistent with sequestration of the protein on the colloidal particles. These observations suggest that colloidal aggregation is common among cell-based antiviral drug repurposing and offers rapid counter-screens to detect and eliminate these artifacts.

Novel Multifunctional Meta-Surface Plasmon Resonance Chip Microplate for High-Throughput Molecular Screening.

The utilization of surface plasmon resonance (SPR) sensors for real-time label-free molecular interaction analysis is already being employed in the fields of in vitro diagnostics and biomedicine. However, the widespread application of SPR technology is hindered by its limited detection throughput and high cost. To address this issue, this study introduces a novel multifunctional MetaSPR high-throughput microplate biosensor featuring 3D nanocupsarray structure, aiming to achieve high-throughput screeningwith a reduced cost and enhanced speed. Different types of MetaSPR sensors and analytical detection methods have been developed for accurateantibody subtype identification, epitope binding, affinity determination, antibody collocation, and quantitative detection， greatly promoting the screening and analysis ofearly-stageantibody drugs. The MetaSPR platform combined with nano-enhanced particles amplifies the detection signal and improves the detection sensitivity, making it more convenient, sensitive, and efficient than traditional ELISA. The findings demonstrate that the MetaSPR biosensor is a new practical technology detection platform that can improve the efficiency of biomolecular interaction studies with unlimited potential for new drug development.

Cerebrospinal Fluid Neutrophil Gelatinase-Associated Lipocalin as a Novel Biomarker for Postneurosurgical Bacterial Meningitis: A Prospective Observational Cohort Study

BACKGROUND AND OBJECTIVES: Postneurosurgical bacterial meningitis (PNBM) was a significant clinical challenge, as early identification remains difficult. This study aimed to explore the potential of neutrophil gelatinase-associated lipocalin (NGAL) as a novel biomarker for the early diagnosis of PNBM in patients who have undergone neurosurgery. METHODS: A total of 436 postneurosurgical adult patients were enrolled in this study. Clinical information, cerebrospinal fluid (CSF), and blood samples were collected. After the screening, the remaining 267 patients were divided into the PNBM and non-PNBM groups, and measured CSF and serum NGAL levels to determine the diagnostic utility of PNBM. Subsequently, patients with PNBM were categorized into gram-positive and gram-negative bacterial infection groups to assess the effectiveness of CSF NGAL in differentiating between these types of infections. We analyzed the changes in CSF NGAL expression before and after anti-infection treatment in PNBM. Finally, an additional 60 patients were included as an independent validation cohort to further validate the diagnostic performance of CSF NGAL. RESULTS: Compared with the non-PNBM group, CSF NGAL was significantly higher in the PNBM group (305.1 [151.6-596.5] vs 58.5 [30.7-105.8] ng/mL; P < .0001). The area under the curve of CSF NGAL for diagnosing PNBM was 0.928 (95% CI: 0.897-0.960), at a threshold of 119.7 ng/mL. However, there was no significant difference in serum NGAL between the 2 groups (142.5 [105.0-248.6] vs 161.9 [126.6-246.6] ng/mL, P = .201). Furthermore, CSF NGAL levels were significantly higher in patients with gram-negative bacterial infections than those with gram-positive bacteria (P = .023). In addition, CSF NGAL levels decrease after treatment compared with the initial stage of infection (P < .0001). Finally, in this validation cohort, the threshold of 119.7 ng/mL CSF NGAL shows good diagnostic performance with a sensitivity and specificity of 90% and 80%, respectively. CONCLUSION: CSF NGAL holds promise as a potential biomarker for the diagnosis, early drug selection, and efficacy monitoring of PNBM.

An ultrasensitive electrochemical/colorimetric dual-mode self-powered biosensing platform for lung cancer marker detection by multiple-signal amplification strategy

BackgroundIn recent years, miRNAs have emerged as potentially valuable tumor markers, and their sensitive and accurate detection is crucial for early screening and diagnosis of tumors. However, the analysis of miRNAs faces significant challenges due to their short sequence, susceptibility to degradation, high similarity, low expression level in cells, and stringent requirements for in vitro research environments. Therefore, the development of sensitive and efficient new methods for the detection of tumor markers is crucial for the early intervention of related tumors. ResultsAn ultrasensitive electrochemical/colorimetric dual-mode self-powered biosensor platform is established to detect microRNA-21 (miR-21) via a multi-signal amplification strategy. Gold nanoparticles (AuNPs) and VS4 nanosheets self-assembled 3D nanorods (VS4-Ns-Nrs) are prepared for constructing a superior performance enzyme biofuel cell (EBFC). The double-signal amplification strategy of Y-shaped DNA nanostructure and catalytic hairpin assembly (CHA) is adopted to further improve enhance the strength and specificity of the output signal. In addition, a capacitor is matched with EBFC to generate an instantaneous current that is amplified several times, and the output detection signal is improved once more. At the same time, electrochemical and colorimetric methods are used for dual-mode strategy to achieve the accuracy of detection. The linear range of detection is from 0.001 pg/mL to 1000 pg/mL, with a relatively low limit of detection (LOD) of 0.16 fg/mL (S/N = 3). SignificanceThe established method enables accurate and sensitive detection of markers in patients with lung cancer, providing technical support and data reference for precise identification. It is anticipated to offer a sensitive and practical new technology and approach for early diagnosis, clinical treatment, and drug screening of cancer and other related major diseases.

Adaptive designs were primarily used but inadequately reported in early phase drug trials

BackgroundFaced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist.MethodsWe searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting.ResultsOur survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated β = 0.14, p < 0.01) and the multicenter trials (estimated β = 2.22, p < 0.01) were associated with better reporting.ConclusionAdaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting.

Benchmarking compound activity prediction for real-world drug discovery applications

Identifying active compounds for target proteins is fundamental in early drug discovery. Recently, data-driven computational methods have demonstrated promising potential in predicting compound activities. However, there lacks a well-designed benchmark to comprehensively evaluate these methods from a practical perspective. To fill this gap, we propose a Compound Activity benchmark for Real-world Applications (CARA). Through carefully distinguishing assay types, designing train-test splitting schemes and selecting evaluation metrics, CARA can consider the biased distribution of current real-world compound activity data and avoid overestimation of model performances. We observed that although current models can make successful predictions for certain proportions of assays, their performances varied across different assays. In addition, evaluation of several few-shot training strategies demonstrated different performances related to task types. Overall, we provide a high-quality dataset for developing and evaluating compound activity prediction models, and the analyses in this work may inspire better applications of data-driven models in drug discovery.

In-situ evaluation the fluctuation of hypochlorous acid in acute liver injury mice models with a mitochondria-targeted NIR ratiometric fluorescent probe

Acute liver injury (ALI) is a frequent and devastating liver disease that has been made more prevalent by the excessive use of chemicals, drugs, and alcohol in modern life. Hypochlorous acid (HClO), an important biomarker of oxidative stress originating mainly from the mitochondria, has been shown to be intimately connected to the development and course of ALI. Herein, a novel BODIPY-based NIR ratiometric fluorescent probe Mito-BS was constructed for the specific recognition of mitochondrial HClO. The probe Mito-BS can rapidly respond to HClO within 20 s with a ratiometric fluorescence response (from 680 nm to 645 nm), 24-fold fluorescence intensity ratio enhancement (I645/I680), a wide pH adaptation range (5−9) and the low detection limit (31 nM). The probe Mito-BS has been effectively applied to visualize endogenous and exogenous HClO fluctuations in living zebrafish and cells based on its low cytotoxicity and prominent mitochondria-targeting ability. Furthermore, the fluorescent probe Mito-BS makes it possible to achieve the non-invasive in-situ diagnosis of ALI through in mice, and provides a feasible strategy for early diagnosis and drug therapy of ALI and its complications.

Early use of oral antiviral drugs and the risk of post COVID-19 syndrome: A systematic review and network meta-analysis

This study aimed to determine the association of early use of oral antiviral drugs (including nirmatrelvir-ritonavir and molnupiravir) with the risk of post COVID-19 condition (PCC) and compare the possible efficacy of nirmatrelvir-ritonavir and molnupiravir. PubMed, Web of Science, Embase, Cochrane, MedRxiv, and Psycinfo were searched from inception until November 1, 2023. We included studies that assessed the effect of oral antiviral drugs on the incidence of PCC. Pairwise and network meta-analyses were conducted using a random-effects model. Risk ratios (RRs) for oral antiviral drugs were calculated with a confidence interval (CI). Nine observational studies containing 866,066 patients were included. Nirmatrelvir-ritonavir and molnupiravir were evaluated in eight and two studies respectively, with both drugs evaluated in one study. Pair-wise meta-analysis showed that early oral antiviral drugs reduced PCC risk (RR 0.77, 95% CI 0.68-0.88). Network meta-analysis showed that nirmatrelvir-ritonavir may perform better than molnupiravir (surface under the cumulative ranking curve: 95.5% vs. 31.6%) at reducing PCC risk. Early use of oral antiviral drugs may potentially protect against developing PCC in non-hospitalized patients with COVID-19. These findings support the standardized administration of oral antiviral drugs in patients during the acute phase of COVID-19 according to the guidelines.

The Association of Docetaxel Side Effects and Introduction of Subsequent Cabazitaxel for Castration-Resistant Prostate Cancer : A Clinical Study

The administration of cabazitaxel for patients with castration-resistant prostate cancer (CRPC) requires prior docetaxel therapy. Sequential chemotherapy may have to be discontinued due to docetaxelassociated side effects. This study investigated the relationship between treatment outcome of docetaxel and cabazitaxel and their associated side effects. We retrospectively analyzed 69 patients with CRPC who had been administered docetaxel withand without subsequent cabazitaxel at Toyonaka Municipal Hospital from October 2014 to June 2022. Twenty-eight patients (41%) discontinued docetaxel because of side effects, and the median number of docetaxel cycles at discontinuation was 2 (range : 1-11). Fourteen of these patients received no treatment following docetaxel. A comparison of the 28 patients who had discontinued docetaxel due to side effects with 41 patients who had not revealed a significant difference in the total numbers of chemotherapy cycles (2.5 vs 9 ; P＜0.001) and time to treatment failure (56 days vs 301 days ; P＝ 0.001), with a trend toward shorter overall survival from the start of docetaxel treatment (259 days vs 512 days ; P＝0.06). Multivariate analysis identified discontinuation of docetaxel due to side effects (OR＝0.07 ; P＜0.001) and lower hemoglobin (OR＝0.01 ; P＝0.001) as significant factors inhibiting the introduction of cabazitaxel. Reducing the side effects of docetaxel, including early drug switching, may allow more CRPC patients to be reached with cabazitaxel. Consequently, the resulting taxane-based chemotherapy may contribute to an additional survival advantage.

A Review on Artificial Intelligence in Pharmaceutical Science

Abstract: In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world data mining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice. Artificial intelligence is the upcoming technology in advance health care system. Current digitalization of medicine and availability of electronic health records (EHRs) has inspired clinical researchers and healthcare personnel to acquire artificial intelligence (AI) methodologies for big data analytics and for very large scale medical databases. The major advantage of AI is that it reduces the time that is needed for drug development and, in turn, it reduces the costs that are associated with drug development, enhances the returns on investment and may even cause a decrease in cost for the end user. A large number of researches are being carried out to improve the current available AI technology to make the pharmacy profession more efficient. The present article briefly describes the importance of AI in the process of drug development and then looks at the various AI tools that are available at the disposal of a modern-day pharmacist to aid in a more efficient functioning.

Late-Onset Molybdenum Cofactor Deficiency Type A: A Treatable Cause of Developmental Delay.

Molybdenum cofactor deficiency classically presents in neonates with intractable seizures; however, milder cases generally present before age 2 years with developmental delays and may go undiagnosed. Early diagnosis, and safe, US Food and Drug Administration-approved substrate replacement are critical to preserve neurologic function. This article discusses 2 children who presented with late-onset molybdenum cofactor deficiency type A.

Human Pharmacokinetic and CYP3A Drug-Drug Interaction Prediction of GDC-2394 Using Physiologically Based Pharmacokinetic Modeling and Biomarker Assessment.

Physiologically based pharmacokinetic (PBPK) modeling was used to predict the human pharmacokinetics and drug-drug interaction (DDI) of GDC-2394. PBPK models were developed using in vitro and in vivo data to reflect the oral and intravenous PK profiles of mouse, rat, dog, and monkey. The learnings from preclinical PBPK models were applied to a human PBPK model for prospective human PK predictions. The prospective human PK predictions were within 3-fold of the clinical data from the first-in-human study, which was used to optimize and validate the PBPK model and subsequently used for DDI prediction. Based on the majority of PBPK modeling scenarios using the in vitro CYP3A induction data (mRNA and activity), GDC-2394 was predicted to have no-to-weak induction potential at 900 mg twice daily (BID). Calibration of the induction mRNA and activity data allowed for the convergence of DDI predictions to a narrower range. The plasma concentrations of the 4β-hydroxycholesterol (4β-HC) were measured in the multiple ascending dose study to assess the hepatic CYP3A induction risk. There was no change in plasma 4β-HC concentrations after 7 days of GDC-2394 at 900 mg BID. A dedicated DDI study found that GDC-2394 has no induction effect on midazolam in humans, which was reflected by the totality of predicted DDI scenarios. This work demonstrates the prospective utilization of PBPK for human PK and DDI prediction in early drug development of GDC-2394. PBPK modeling accompanied with CYP3A biomarkers can serve as a strategy to support clinical pharmacology development plans. SIGNIFICANCE STATEMENT: This work presents the application of physiologically based pharmacokinetic modeling for prospective human pharmacokinetic (PK) and drug-drug interaction (DDI) prediction in early drug development. The strategy taken in this report represents a framework to incorporate various approaches including calibration of in vitro induction data and consideration of CYP3A biomarkers to inform on the overall CYP3A-related DDI risk of GDC-2394.

Multimodal Mass Spectrometry Imaging of an Osteosarcoma Multicellular Tumour Spheroid Model to Investigate Drug-Induced Response.

A multimodal mass spectrometry imaging (MSI) approach was used to investigate the chemotherapy drug-induced response of a Multicellular Tumour Spheroid (MCTS) 3D cell culture model of osteosarcoma (OS). The work addresses the critical demand for enhanced translatable early drug discovery approaches by demonstrating a robust spatially resolved molecular distribution analysis in tumour models following chemotherapeutic intervention. Advanced high-resolution techniques were employed, including desorption electrospray ionisation (DESI) mass spectrometry imaging (MSI), to assess the interplay between metabolic and cellular pathways in response to chemotherapeutic intervention. Endogenous metabolite distributions of the human OS tumour models were complemented with subcellularly resolved protein localisation by the detection of metal-tagged antibodies using Imaging Mass Cytometry (IMC). The first application of matrix-assisted laser desorption ionization-immunohistochemistry (MALDI-IHC) of 3D cell culture models is reported here. Protein localisation and expression following an acute dosage of the chemotherapy drug doxorubicin demonstrated novel indications for mechanisms of region-specific tumour survival and cell-cycle-specific drug-induced responses. Previously unknown doxorubicin-induced metabolite upregulation was revealed by DESI-MSI of MCTSs, which may be used to inform mechanisms of chemotherapeutic resistance. The demonstration of specific tumour survival mechanisms that are characteristic of those reported for in vivo tumours has underscored the increasing value of this approach as a tool to investigate drug resistance.

Fluorescence Thermal Shift Assay based High Through-put Screening in Small Molecule Drug Discovery: A Brief Overview

Background: The Early drug discovery process was majorly phenotypic, lacking target specificity, and mechanism of action causing short and/or long-term toxicological impacts on the patients leading to several drug withdrawals from the market. Biochemical procedures and methods used in drug discovery and development make it lengthy in terms of time requiring approx. 15 years with the investment of billions of dollars for a new drug molecule. Technological advancement leading to the identification of protein structures through crystallography paved a new path toward targeted drug discovery. Objective: This paper provides a brief overview of the use of the fluorescence thermal shift assay (FTSA) as a high throughput (HTS) screening technique in small molecule drug discovery. Methods: The article discusses the principles of thermal denaturation techniques and how they are applied in drug discovery. It highlights the advantages of FTSA over other biophysical methods, including its label-free, cost-effective, and high HTS nature. The review also presents a comparative analysis of FTSA with techniques such as isothermal titration calorimetry (ITC), mass spectrometry (MS), nuclear magnetic resonance (NMR), surface plasmon resonance (SPR), X-ray crystallography(X-RD), circular dichroism (CD), and differential scanning calorimetry (DSC). Results: The FTSA technique is described as a powerful tool that addresses the limitations of larger protein requirements, specific assay development, and low HTS associated with other thermal denaturation methods. Unlike biochemical and cellular assays that measure secondary effects, FTSA directly estimates the binding of small molecules to target proteins. Conclusion: The review concludes that FTSA is the only biophysical method that satisfies the three key prerequisites of being label-free, cost-effective, and suitable for high HTS screening in small molecule drug discovery.

#1460 Targeting cyst progression: evaluation of anthelmintic and proton pump inhibitor in a rodent PKD model

Abstract Background and Aims In polycystic kidney disease (PKD) cyst formation and growth leads to progressive kidney damage. Recognizing the need for effective cyst-inhibitory drugs, we previously performed high-content screening of approved, repurposable drugs in a zebrafish model and identified several compounds with cyst-inhibitory activity and low toxicity. Here, we further tested two of the identified compounds, an anthelmintic agent and proton pump inhibitor, in a rodent model of PKD (PCK rat) for their cyst inhibitory and GFR-preserving capacity in mammals. Method The identified compounds were administered to 3-week-old male and female PCK rats for a total of 10 weeks to evaluate early drug effects on kidney function. Untreated PCK rats fed a normal ROD16 diet served as controls. After 5 weeks of treatment, total kidney and cystic volume and number of cysts were assessed in vivo by ultrasound. Following 10 weeks of treatment, kidney function was assessed by transcutaneous measurement of FITC-Sinistrin. After perfusion fixation in the same week, the kidneys, liver, heart, and pancreas were harvested for subsequent histological and molecular biological analyses. Results In male rats, 5-week proton pump inhibitor treatment was associated with a 50% reduction in the number of cysts (p &amp;lt; 0.05) and a nominal 22% reduction of fractional cystic kidney volume (CKV). Females showed a 16% reduction in cyst count and a modest reduction in fractional CKV. Treatment with the anthelmintic tended to reduce fractional CKV and cyst count in female animals whereas fractional cyst volume in males increased. After 10 weeks of treatment, a trend towards better preservation of GFR was observed for both therapies. Conclusion In conclusion, our study demonstrates a potential therapeutic benefit of a proton pump inhibitor and an anthelmintic agent in attenuating the progression of polycystic kidney disease (PKD) in a PCK rat model. The observed reductions in fractional cystic kidney volume and cyst count, coupled with marginal improvements in renal function, document a potential therapeutic effect of these compounds. Our preliminary findings justify further exploration of novel treatment modalities targeting cyst progression and renal dysfunction in this challenging clinical condition.

An ultrasensitive electrochemical biosensor based on logic transcription circuit-activated DNAzyme amplifier for flap endonuclease 1 activity detection

The accurate detection of flap endonuclease 1 (FEN1) activity is crucial for early cancer diagnosis and therapy. Herein, we have established a “target-substrate double-enhanced” DNA electrochemical biosensor by integrating a cleavage-ligation logic transcription circuit-activated DNAzyme amplifier with the transition-metal carbides-gold nanoparticles (MXene-Au) composites for the accurate detection of FEN1 activity. The FEN1 can be translated into a stable transcription hairpin DNA (TH), incorporating the catalytic strand of the DNAzyme via the logic transcription circuit. Then, the TH hybridizes with the substrate strand of DNAzyme and the DNAzyme amplifier can be activated, thus producing a significant quantity of single-stranded DNA (sDNA) and achieving the efficient amplification of the FEN1. Finally, the sDNA loaded with electroactive substances is immobilized on the capture DNA-anchored MXene-Au modified electrode, significantly enhancing the electrochemical signal intensity for highly sensitive FEN1 activity detection. The excellent selectivity and sensitivity of the proposed electrochemical biosensor can facilitate the accurate detection of FEN1 activity in biological samples and aid in screening FEN1-releated drugs. Additionally, three-input concatenated logic gates (AND-AND, INHIBIT-AND, and AND-OR) are constructed for FEN1 activity detection. Particularly, the AND-AND gate achieves logic detection of FEN1 activity in complex samples. Consequently, this bidirectionally enhanced electrochemical biosensor offers a novel analytical method for FEN1 activity detection, showing substantial promise for early diagnosis and drug discovery of FEN1-related disease.

Vitamin C for all?

Vitamin C can be a potential adjunctive treatment option for critically ill individuals due to its pleiotropic effects as electron donor in many enzymatic reactions throughout the body. Recently, several important randomized controlled trials (RCTs) investigating vitamin C in critically ill patients have been published. Two recent large RCTs administering high-dose vitamin C to patients with sepsis and COVID-19 showed signs of harm. Though performed at high standard, these trials had several limitations. Recent studies in cardiac surgery and burns showed decreased cardiac enzymes and improved clinical outcomes after cardiac surgery, and decreased fluid requirements, reduced wound healing time and in-hospital mortality after burns. Vitamin C may hold benefit in the management of other ischemia/reperfusion injury populations, including postcardiac arrest patients and after solid organ transplantation. Currently, covering basal vitamin C requirements during critical illness is recommended, though the exact dose remains to be determined. Future work should address optimal vitamin C timing, since early versus late drug administration are likely distinct, and duration of therapy, where withdrawal-induced injury is possible. Additionally accurate assessment of body stores with determination of individual vitamin requirements is crucial to ascertain patient and subgroups most likely to benefit from vitamin C.

STOPLIGHT: A Hit Scoring Calculator.

We introduce STOPLIGHT, a web portal to assist medicinal chemists in prioritizing hits from screening campaigns and the selection of compounds for optimization. STOPLIGHT incorporates services to assess 6 physiochemical and structural properties, 6 assay liabilities, and 11 pharmacokinetic properties, for any small molecule represented by its SMILES string. We briefly describe each service and illustrate the utility of this portal with a case study. The STOPLIGHT portal provides a user-friendly tool to guide hit selection in early drug discovery campaigns, whereby compounds with unfavorable properties can be quickly recognized and eliminated.

Exploring the unexplored chemical space: Rational identification of new Tafenoquine analogs with antimalarial properties

Patents tend to define a huge chemical space described by the combinatorial nature of Markush structures. However, the optimization of new principal active ingredient is frequently driven by a simple Free Wilson approach. This procedure leads to a highly focused study on the chemical space near a hit compound leaving many unexplored regions that may present highly biological active reservoirs. This study aims to demonstrate that this unveiled chemical space can hide compounds with interesting potential biological activity that would be worth pursuing. This underlines the value and necessity of broadening an approach beyond conventional strategies. Hence, we advocate for an alternative methodology that may be more efficient in the early drug discovery stages. We have selected the case of Tafenoquine, a single-dose treatment for the radical cure of P. vivax malaria approved by the FDA in 2018, as an example to illustrate the process. Through the deep exploration of the Tafenoquine chemical space, seven compounds with potential antimalarial activity have been rationally identified and synthesized. This small set is representative of the chemical diversity unexplored by the 58 analogs reported to date. After biological assessment, results evidence that our approach for rational design has proven to be a very efficient exploratory methodology suitable for the early drug discovery stages.