Identification Of Drugs Research Articles

Disruption of the ZFP574–THAP12 complex suppresses B cell malignancies in mice

Despite the availability of life-extending treatments for B cell leukemias and lymphomas, many of these cancers remain incurable. Thus, the development of new molecular targets and therapeutics is needed to expand treatment options. To identify new molecular targets, we used a forward genetic screen in mice to identify genes required for development or survival of lymphocytes. Here, we describe Zfp574, an essential gene encoding a zinc finger protein necessary for normal and malignant lymphocyte survival. We show that ZFP574 interacts with zinc finger protein THAP12 and promotes the G1-to-S-phase transition during cell cycle progression. Mutation of ZFP574 impairs nuclear localization of the ZFP574-THAP12 complex. ZFP574 or THAP12 deficiency results in cell cycle arrest and impaired lymphoproliferation. Germline mutation, acute gene deletion, or targeted degradation of ZFP574 suppressed Myc-driven B cell leukemia in mice, but normal B cells were largely spared, permitting long-term survival, whereas complete lethality was observed in control animals. Our findings support the identification of drugs targeting ZFP574-THAP12 as a unique strategy to treat B cell malignancies.

A multi-biomarker micronucleus assay using imaging flow cytometry

Genetic toxicity testing assesses the potential of compounds to cause DNA damage. There are many genetic toxicology screening assays designed to assess the DNA damaging potential of chemicals in early drug development aiding the identification of promising drugs that have low-risk potential for causing genetic damage contributing to cancer risk in humans. Despite this, in vitro tests generate a high number of misleading positives, the consequences of which can lead to unnecessary animal testing and/or the abandonment of promising drug candidates. Understanding chemical Mode of Action (MoA) is vital to identifying the true genotoxic potential of substances and, therefore, the risk translation into the clinic. Here we demonstrate a simple, robust protocol for staining fixed, human-lymphoblast p53 proficient TK6 cells with antibodies against ɣH2AX, p53 and pH3S28 along with DRAQ5™ DNA staining that enables analysis of un-lysed cells via microscopy approaches such as imaging flow cytometry. Here, we used the Cytek® Amnis® ImageStream®X Mk II which provides a high-throughput acquisition platform with the sensitivity of flow cytometry and spatial morphological information associated with microscopy. Using the ImageStream manufacturer’s software (IDEAS® 6.2), a masking strategy was developed to automatically detect and quantify micronucleus events (MN) and characterise biomarker populations. The gating strategy developed enables the generation of a template capable of automatically batch processing data files quantifying cell-cycle, MN, ɣH2AX, p53 and pH3 populations simultaneously. In this way, we demonstrate how a multiplex system enables DNA damage assessment alongside MN identification using un-lysed cells on the imaging flow cytometry platform. As a proof-of-concept, we use the tool chemicals carbendazim and methyl methanesulphonate (MMS) to demonstrate the assay’s ability to correctly identify clastogenic or aneugenic MoAs using the biomarker profiles established.

Atomically Precise Chiral Metal Nanoclusters for Circularly Polarized Light Detection

AbstractCircularly polarized light (CPL) detection is of great significance in various applications such as drug identification, sensing and imaging. Atomically precise chiral metal nanoclusters with intense circular dichroism (CD) signals are promising candidates for CPL detection, which can further facilitate device miniaturization and integration. Herein, we report the preparation of a pair of optically active chiral silver nanoclusters [Ag7(R/S‐DMA)2(dpppy)3] (BF4)3 (R/S‐Ag7) for direct CPL detection. The crystal structure and molecular formula of R/S‐Ag7 clusters are confirmed by single‐crystal X‐ray diffraction and high‐resolution mass spectrometry. R/S‐Ag7 clusters exhibit strong CD spectra and CPL both in solution and solid states. When used as the photoactive materials in photodetectors, R/S‐Ag7 enables effective discrimination between left‐handed circularly polarized and right‐handed circularly polarized light at 520 nm with short response time, high responsivity and considerable discrimination ratio. This study is the first report on using atomically precise chiral metal nanoclusters for CPL detection.

Atomically Precise Chiral Metal Nanoclusters for Circularly Polarized Light Detection.

Circularly polarized light (CPL) detection is of great significance in various applications such as drug identification, sensing and imaging. Atomically precise chiral metal nanoclusters with intense circular dichroism (CD) signals are promising candidates for CPL detection, which can further facilitate device miniaturization and integration. Herein, we report the preparation of a pair of optically active chiral silver nanoclusters [Ag7(R/S-DMA)2(dpppy)3] (BF4)3 (R/S-Ag7) for direct CPL detection. The crystal structure and molecular formula of R/S-Ag7 clusters are confirmed by single-crystal X-ray diffraction and high-resolution mass spectrometry. R/S-Ag7 clusters exhibit strong CD spectra and CPL both in solution and solid states. When used as the photoactive materials in photodetectors, R/S-Ag7 enables effective discrimination between left-handed circularly polarized and right-handed circularly polarized light at 520 nm with short response time, high responsivity and considerable discrimination ratio. This study is the first report on using atomically precise chiral metal nanoclusters for CPL detection.

Human and Murine Cell Lines for Adrenocortical Carcinoma and Pheochromocytoma

Adrenocortical carcinoma (ACC) and pheochromocytoma (PCC) are malignancies originating from distinct layers of the adrenal gland. ACCs arise from the adrenal cortex, are often detected at advanced stages and are associated with poor prognosis. PCCs are mostly benign, arise from the adrenal medulla and have a variable prognosis, with 10% of PCCs resulting in metastasis. Genetic background strongly influences metastasis of PCCs, and no reliable biomarkers that predict metastatic behavior exist to date. Current therapeutic strategies for both ACCs and PCCs are overall limited. Thus, novel preclinical models and drug screening approaches need to be established to aid in the identification of more promising drugs and treatment schemes. In this review, we summarize the currently available human and murine cell lines for both tumor entities.

Exploring the Recent Pioneering Developments of Small Molecules in Antimalarial Drug Armamentarium: A Chemistry Prospective Appraisal.

Malaria is a very destructive and lethal parasitic disease that causes significant mortality worldwide, resulting in the loss of millions of lives annually. It is an infectious disease transmitted by mosquitoes, which is caused by different species of the parasite protozoan belonging to the genus Plasmodium. The uncontrolled intake of antimalarial drugs often employed in clinical settings has resulted in the emergence of numerous strains of plasmodium that are resistant to these drugs, including multidrug-resistant strains. This resistance significantly diminishes the effectiveness of many primary drugs used in the treatment of malaria. Hence, there is an urgent need for developing unique classes of antimalarial drugs that function with distinct mechanisms of action. In this context, the design and development of hybrid compounds that combine pharmacophoric properties from different lead molecules into a single unit gives a unique perspective towards further development of malaria drugs in the next generation. In recent years, the field of medicinal chemistry has made significant efforts resulting in the discovery and synthesis of numerous small novel compounds that exhibit potent antimalarial properties, while also demonstrating reduced toxicity and desirable efficacy. In light of this, we have reviewed the progress of hybrid antimalarial agents from 2021 up to the present. This manuscript presents a comprehensive overview of the latest advancements in the medicinal chemistry pertaining to small molecules, with a specific focus on their potential as antimalarial agents. As possible antimalarial drugs that might target both the dual stage and multi-stage stages of the parasite life cycle, these small hybrid molecules have been studied. This review explores a variety of physiologically active compounds that have been described in the literature in order to lay a strong foundation for the logical design and eventual identification of antimalarial drugs based on lead frameworks.

A collaborative pharmacist-led intervention to prevent hospital readmissions among elderly patients discharged from the emergency department: a retrospective cohort study

Unplanned hospital readmission is a safety and quality healthcare measure, conferring significant costs to the healthcare system. Elderly individuals, particularly, are at high risk of readmissions, often due to issues related to medication management. Pharmacists play a pivotal role in addressing medication-related concerns, which can potentially reduce readmissions. This retrospective single-centre cohort study, conducted from November 2022 to February 2023 in an emergency department, aimed to determine if integrating emergency medicine pharmacists into Emergency Department care models reduces unplanned hospital readmissions within 28 days and to identify the interventions they employ. The inclusion criteria included patients aged ≥ 65, taking ≥ 3 medications, and presenting with falls, cognition changes, or reduced mobility and were planned for discharge to home from the emergency department. Collaborating with the Emergency Department Aged Care Service Emergency Team, a pharmacist provided comprehensive medication management consultations, discharge liaison services, and other pharmacy related interventions to eligible participants whenever the pharmacist was available. Patients who met the eligibility criteria but did not receive pharmacist interventions due to the pharmacist's unavailability served as the control group. This method was chosen to ensure that the control group consisted of comparable patients who only differed in terms of receiving the pharmacist intervention. The study included 210 participants, with 120 receiving pharmacist interventions and 90 acting as controls. The results revealed a significant reduction in unplanned hospital readmissions among participants who received pharmacist interventions (10.0%, n = 12) compared to controls (22.2%, n = 20), with a notable difference of 12.2% (95% confidence interval 2.4–23.4%, p = 0.01). A total of 107 interventions were documented, emphasising medication selection recommendations (28.0%) and identification of adverse drug reactions/drug-drug interactions (21.5%) as primary areas of focus. These findings suggest that integrating skilled pharmacists into Emergency Department Aged Care Service Emergency Team (ASET) lowered the rate of unplanned hospital readmission within 28 days resulting in improved hospital performance metric outcomes. This highlights the potential role of pharmacists in addressing medication-related issues and enhancing the quality and safety of healthcare delivery, particularly for elderly patients transitioning from the ED to home care settings.

X-ray absorption spectroscopy combined with deep learning for auto and rapid illicit drug detection

ABSTRACT Background: X-ray absorption spectroscopy (XAS) is a widely used substance analysis technique. It bases on the different absorption coefficients at different energy level to achieve material identification. Additionally, the combination of spectral technology and deep learning can achieve auto detection and high accuracy in material identification. Objectives: Current methods are difficult to identify drugs quickly and nondestructively. Therefore, we explore a novel approach utilizing XAS for the detection of prohibited drugs with common X-ray tube source and photon-counting (PC) detector. Method: To achieve automatic, rapid, and accurate detection of drugs. A CdTe detector and a common X-ray source were used to collect data, then dividing the data into training and testing sets. Finally, the improved transformer encoder model was used for classification. LSTM and ResU-net models are selected for comparation. Result: Fifty substances, which are isomers or compounds with similar molecular formulas of drugs, were selected for experiment substances. The results showed that the improved transformer model achieving 1.4 hours for training time and 96.73% for accuracy, which is better than the LSTM (2.6 hours and 65%) and ResU-net (1.5 hours and 92.7%). Conclusion: It can be concluded that the attention mechanism is more accurate for spectral material identification. XAS combined with deep learning can achieve efficient and accurate drug identification, offering promising application in clinical drug testing and drug enforcement.

Identification and Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors for Therapeutic Drug Monitoring

Identification and Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors for Therapeutic Drug Monitoring

Identifying High-Quality Leads among Screened Anticancerous Compounds Using SMILES Representations.

Cancer is a lethal disease that affects numerous people worldwide. Chemotherapy stands as one of the most effective treatment regimens to combat cancer. Nevertheless, anticancer drugs face a high failure rate due to safety and efficacy issues. Drug failure could be subdued by instigating drug leads with reduced toxicity and enhanced efficacy. Computer-aided drug discovery endorses drug leads in manoeuvring protein and ligand structures or representations. Simplified molecular input line entry system (SMILES) is a linear notation representing the three-dimensional structure of a molecule using symbols and alphanumeric characters. SMILES representation hoards rings and scaffold structures in its depiction. Mining ring and scaffold patterns from molecular SMILES would assist in ascertaining biological properties based on molecular patterns. Moreover, the emergence of artificial intelligence (AI) technologies would accelerate identification of efficient anticancer drug leads. AI algorithms proclaimed for their pattern recognition ability could be employed for identifying molecular patterns from SMILES representation, thereby enabling property prediction. Consequently, we developed a multilayer perceptron (MLP) model for the prediction of anticancer activity using SMILES of NCI-60 cancer growth inhibition data. Furthermore, the top 8 frequent scaffolds were identified on preliminary analysis of cancer growth inhibition data and ChEMBL drugs. The developed MLP model classified anticancer and nonanticancer compounds with a classification accuracy of 0.92. Also, benchmarking of the developed model with machine learning algorithms exhibited better performance of the MLP model.

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking.

Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechanistic relationships between immune response and hypertensive LVH or to find novel therapeutic targets. This study aimed to screen hub immune-related genes involved in hypertensive LVH as well as to explore immune target-based therapeutic drugs. RNA-sequencing data from a mouse model generated by angiotensin II infusion were subjected to weighted gene co-expression network analysis (WGCNA) to identify core expression modules. Machine learning algorithms were applied to screen immune-related LVH characteristic genes. Heart structures were evaluated by echocardiography and cardiac magnetic resonance imaging (CMRI). Validation of hub genes was conducted by RT-qPCR and western blot. Using the Connectivity Map database and molecular docking, potential small-molecule drugs were explored. A total of 1215 differentially expressed genes were obtained, most of which were significantly enriched in immunoregulation and collagen synthesis. WGCNA and multiple machine learning strategies uncovered six hub immune-related genes (Ankrd1, Birc5, Nuf2, C1qtnf6, Fcgr3, and Cdca3) that may accurately predict hypertensive LVH diagnosis. Immune analysis revealed that fibroblasts and macrophages were closely correlated with hypertensive LVH, and hub gene expression was significantly associated with these immune cells. A regulatory network of transcription factor-mRNA and a ceRNA network of miRNA-lncRNA was established. Notably, six hub immune-related genes were significantly increased in the hypertensive LVH model, which were positively linked to left ventricle wall thickness. Finally, 12 small-molecule compounds with the potential to reverse the high expression of hub genes were ruled out as potential therapeutic agents for hypertensive LVH. This study identified and validated six hub immune-related genes that may play essential roles in hypertensive LVH, providing new insights into the potential pathogenesis of cardiac remodeling and novel targets for medical interventions.

Advantages and Challenges of Platform Trials for Disease Modifying Therapies in Parkinson's Disease.

Traditional drug development in Parkinson's disease (PD) faces significant challenges because of its protracted timeline and high costs. In response, innovative master protocols are emerging and designed to address multiple research questions within a single overarching protocol. These trials may offer advantages such as increased efficiency, agility in adding new treatment arms, and potential cost savings. However, they also present organizational, methodological, funding, regulatory, and sponsorship challenges. We review the potential of master protocols, focusing on platform trials, for disease modifying therapies in PD. These trials share a common control group and allow for the termination or addition of treatment arms during a trial with non-predetermined end. Specific issues exist for a platform trial in the PD field considering the heterogeneity of patients in terms of phenotype, genotype and staging, the confounding effects of symptomatic treatments, and the choice of outcome measures with no consensus on a non-clinical biomarker to serve as a surrogate and the slowness of PD progression. We illustrate these aspects using the examples of the main PD platform trials currently in development with each one targeting distinct goals, populations, and outcomes. Overall, platform trials hold promise in expediting the evaluation of potential therapies for PD. However, it remains to be proven whether these theoretical benefits will translate into increased production of high-quality trial data. Success also depends on the willingness of pharmaceutical companies to engage in such trials and whether this approach will ultimately hasten the identification and licensing of effective disease-modifying drugs. © 2024 International Parkinson and Movement Disorder Society.

PDCD4 as a marker of mTOR pathway activation and therapeutic target in mycobacterial infections.

This study emphasizes the critical role of the mammalian target of rapamycin (mTOR) pathway in macrophage responses to mycobacterial infections, elucidating how mycobacteria activate mTOR, resulting in PDCD4 degradation. The utilization of the (Tor)-signal-indicator (TOSI) vector for real-time monitoring of mTOR activity represents a significant advancement in understanding mTOR regulation during mycobacterial infection. These findings deepen our comprehension of mycobacteria's innate immune mechanisms and introduce PDCD4 as a novel marker for mTOR activity in infectious diseases. Importantly, this research laid the groundwork for high-throughput screening of mTOR inhibitors using FDA-approved drugs, offering the potential for repurposing treatments against mycobacterial infections. The identification of drugs that inhibit mTOR activation opens new avenues for host-directed therapies, marking a significant step forward in combating tuberculosis and other mycobacterial diseases.

Onychocolone A produced by the fungus Onychocola sp. targets cancer stem cells and stops pancreatic cancer progression by inhibiting MEK2-dependent cell signaling

Pancreatic cancer (PC) shows a high fatality rate that can only be faced with a combination of surgery and chemotherapy or palliative treatment in the case of advanced patients. Besides, PC tumors are enriched with subpopulations of cancer stem cells (CSCs) that are resistant to the existing chemotherapeutic agents, which raises an important need for the identification of new drugs. To fill this gap, we have tested the anti-tumoral activity of microbial extracts, which chemical diversity offers a broad spectrum of potential new bioactive compounds. Extracts derived from the fungus Onychocola sp. CF-107644 were assayed via high throughput screening followed by bioassay-guided fractionation and resulted in the identification and isolation of six benzophenone derivatives with antitumoral activity: onychocolones A-F (#1-6). The structures of the compounds were established by spectroscopic methods, including ESI-TOF MS, 1D and 2D NMR analyses and X-ray diffraction. Compounds #1-4 significantly inhibited the growth of the pancreas tumoral cell lines, with low-micromolar Median Effective Doses (ED50s). Compound #1 (onychocolone A) was prioritized for further profiling due to its pro-apoptotic effect, which was further validated on 3D spheroids and pancreatic CSCs. Protein expression assays showed that the effect was mechanistically linked to the inhibition of MEK onco-signaling pathway. The efficacy of onychocolone A was also demonstrated in vivo by the reduction of tumor growth in a pancreatic xenograft mouse model generated by CSCs. Altogether, the data support that onychocolone A is a promising new small molecule for hit-to-lead development of a new treatment for PC.

Combining surface-enhanced Raman spectroscopy (SERS) and paper spray mass spectrometry (PS-MS) for illicit drug detection

There is an ongoing effort in the US illicit drug market to make new psychoactive compounds more potent and addictive. Due to continuous chemical modifications, many fentanyl analogs are developed and mixed with more traditional illicit drugs, such as cocaine and heroin. Detecting fentanyl and fentanyl analogs in these illicit drug mixtures has become more crucial because of the increased potency and associated health risks. Most confirmatory procedures require time-consuming and expensive, highly sophisticated laboratory equipment and experimental procedures, which can delay critical information that might save a victim or find a suspect. In this study, we propose miniaturizing and accelerating this process by combining surface-enhanced Raman spectroscopy (SERS) analysis and paper spray mass spectrometry (PS-MS). For this aim, dual-purposed paper substrates were developed through soaking in Au/Ag nanostars suspensions. These novel, in-house prepared paper SERS substrates showed stability for up to four weeks with and without the presence of drug compounds. Fentanyl analogs with similar SERS spectra were differentiated by coupling with PS-MS. The limit of detection (LOD) for fentanyl on the paper substrates is 34 μg/mL and 0.32 μg/mL for SERS and PS-MS, respectively. Fentanyl and fentanyl analogs show selective SERS enhancement that helped to detect trace amounts of these opioids in heroin and cocaine street samples. In short, we propose the combination of SERS/PS-MS by using modified paper substrates to develop cost-effective, sensitive, rapid, portable, reliable, and reproducible methods to detect illicit drugs, especially trace amounts of fentanyl and fentanyl analogs in illicit drug mixtures. The combination of these two category A techniques allows for the identification of illicit drugs according to the SWGDRUG guidelines.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs.

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Quantification of AICAR and study of metabolic markers after administration.

Objectives: AICAR (5-amino-4-imidazolecarboxyamide ribonucleoside) was reported as the first pharmacological AMPK (adenosine 5'-monophosphate (AMP)-activated protein kinase) activator, and it has been confirmed to exhibit a significant endurance enhancement effect and prohibited for doping by the World Anti-Doping Agency. Due to the fact that the human body can produce such substances, in order to ensure fairness in sports competition, methods for rapid detection and multi-type identification of AICAR drugs taken orally should be established. Methods: to assess AICAR levels, a new rapid, sensitive, efficient, and selective method was reported for the quantitative detection of AICAR in urine using LC-MS/MS. The method was validated for quantitative purposes based on the elemental selectivity, intra- (1.0-15.6%) and inter-day precision (1.3-16.3%), accuracy (99.9-112.8%), matrix effects (88.9-103.6%), recovery (87.4-106.5%), and stability at four different concentrations. The calibration curve was linear over a wide concentration range of 10-10,000 ng mL-1 with a high coefficient of determination (R 2 > 0.998). The limit of detection (LOD) and limit of quantification (LOQ) for the experiment were determined to be 1 and 10 ng mL-1, respectively. Simultaneously, metabolomics analysis was used to obtain the metabolic fingerprint of different populations and biomarkers to distinguish administration cases through partial least squares discriminant analysis (PLS-DA) and a receiver operating characteristic (ROC) curve. Results: the method enables easy quantitation for LC-MS/MS analysis with the best recovery yield maintained, and the method was applied to 122 Asian biological samples with an average concentration of 1310.5 ± 1031.4 ng mL-1. Through drug metabolism research, 734 and 294 variables were extracted for data analysis respectively in the positive and negative ion modes, and more than 100 metabolites with significant up- and down-regulation were found after the test. Conclusions: this research developed a fast, precise, effective, and specific approach for the qualitative and quantitative identification of AICAR in urine. Meanwhile, administration metabolism studies found that there were significant changes in AICAR levels and other compounds, such as PC types PC(18:1/16:0), PC(16:0/18:0), and PC(16:0/16:0), PE types PE(18:0/20:4), and LPE-type 18:1, which could better distinguish samples before and after AICAR administration. The analysis provides a multi-perspective reference for WADA to determine a positive criterion.

Two-Dimensional Deep Learning Frameworks for Drug-Induced Cardiotoxicity Detection.

The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in vitro models, have severe limitations in accurate identification of cardiotoxic substances. Pioneering a paradigm shift from these conventional techniques, our study presents two deep learning-based frameworks, STFT-CNN and SST-CNN, to assess cardiotoxicity with markedly improved accuracy and reliability. Leveraging the power of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a more human-relevant cell model, we record mechanical beating signals through impedance measurements. These temporal signals were converted into enriched two-dimensional representations through advanced transformation techniques, specifically short-time Fourier transform (STFT) and synchro-squeezing transform (SST). These transformed data are fed into the proposed frameworks for comprehensive analysis, including drug type classification, concentration classification, cardiotoxicity classification, and new drug identification. Compared to traditional models like recurrent neural network (RNN) and 1-dimensional convolutional neural network (1D-CNN), SST-CNN delivered an impressive test accuracy of 98.55% in drug type classification and 99% in distinguishing cardiotoxic and noncardiotoxic drugs. Its feasibility is further highlighted with a stellar 98.5% average accuracy for classification of various concentrations, and the superiority of our proposed frameworks underscores their promise in revolutionizing drug safety assessments. With a potential for scalability, they represent a major leap in drug safety assessments, offering a pathway to more robust, efficient, and human-relevant cardiotoxicity evaluations.

Phenotypic drug discovery: a case for thymosin alpha-1.

Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.

Assessment of a Single Quadrupole Mass Spectrometer Combined with an Atmospheric Solids Analysis Probe for the On-Site Identification of Amnesty Bin Drugs.

The surging number of people who abuse drugs has a great impact on healthcare and law enforcement systems. Amnesty bin drug analysis helps monitor the "street drug market" and tailor the harm reduction advice. Therefore, rapid and accurate drug analysis methods are crucial for on-site work. An analytical method for the rapid identification of five commonly detected drugs ((3,4-methylenedioxymethamphetamine (MDMA), cocaine, ketamine, 4-bromo-2,5-dimethoxyphenethylamine, and chloromethcathinone)) at various summer festivals in the U.K. was developed and validated employing a single quadrupole mass spectrometer combined with an atmospheric pressure solids analysis probe (ASAP-MS). The results were confirmed on a benchtop gas chromatography-mass spectrometry instrument and included all samples that challenged the conventional spectroscopic techniques routinely employed on-site. Although the selectivity/specificity step of the validation assessment of the MS system proved a challenge, it still produced 93% (N = 279) and 92.5% (N = 87) correct results when tested on- and off-site, respectively. A few "partly correct" results showed some discrepancies between the results, with the MS-only unit missing some low intensity active ingredients (N-ethylpentylone, MDMA) and cutting agents (caffeine, paracetamol, and benzocaine) or detecting some when not present. The incorrect results were mainly based on library coverage. The study proved that the ASAP-MS instrument can successfully complement the spectroscopic techniques used for qualitative drug analysis on- and off-site. Although the validation testing highlighted some areas for improvement concerning selectivity/specificity for structurally similar compounds, this method has the potential to be used in trend monitoring and harm reduction.