Clinical Drug Development Research Articles

Assembly of Dandelion-Like Nanoprobe for Sensitive Detection of N6-Methyladenosine Demethylase by Single-Molecule Counting.

N6-methyladenosine (m6A) demethylase is essential for enzymatically removing methyl groups from m6A modifications and is significantly implicated in the pathogenesis and advancement of various cancers, which makes it a promising biomarker for cancer detection and research. As a proof of concept, we select the fat mass and obesity-associated protein (FTO) as the target m6A demethylase and develop a dandelion-like nanoprobe-based sensing platform by employing biobar-code amplification (BCA) for signal amplification. We construct two meticulously designed three-dimensional structures: reporter-loaded gold nanoparticles (Reporter@Au NPs) and substrate-loaded magnetic microparticles (Substrate@MMPs), which can self-assemble to form dandelion-like nanoprobes via complementary base pairing. In the presence of FTO, the m6A-containing substrates are demethylated, triggering the MazF-assisted cleavage reaction and thereby releasing the Reporter@Au NPs. Furthermore, upon digestion by exonucleases, the Reporter@Au NPs may liberate a significant quantity of Cy3 signals. Remarkably, the combined effects of Au NPs' superior enrichment capacity, MMPs' exceptional magnetic separation efficiency, and the precision of the single-molecule detection platform endow the FTO sensor with exceptional sensitivity and specificity with a detection limit of 7.46 × 10-16 M. Additionally, this method offers a versatile platform for the detection of m6A demethylase and the screening of corresponding inhibitors, thereby advancing clinical diagnosis and drug development.

Twenty-first century mouse genetics is again at an inflection point.

The laboratory mouse has been the premier model organism for biomedical research owing to the availability of multiple well-characterized inbred strains, its mammalian physiology and its homozygous genome, and because experiments can be performed under conditions that control environmental variables. Moreover, its genome can be genetically modified to assess the impact of allelic variation on phenotype. Mouse models have been used to discover or test many therapies that are commonly used today. Mouse genetic discoveries are often made using genome-wide association study methods that compare allelic differences in panels of inbred mouse strains with their phenotypic responses. Here we examine changes in the methods used to analyze mouse genetic models of biomedical traits during the twenty-first century. To do this, we first examine where mouse genetics was before the first inflection point, which was just before the revolution in genome sequencing that occurred 20 years ago, and then describe the factors that have accelerated the pace of mouse genetic discovery. We focus on mouse genetic studies that have generated findings that either were translated to humans or could impact clinical medicine or drug development. We next explore how advances in computational capabilities and in DNA sequencing methodology during the past 20 years could enhance the ability of mouse genetics to produce solutions for twenty-first century public-health problems.

PARP inhibitor drugs and their targeted therapy for BRCA gene mutations

Abstract. Poly ADP-Bibose polymerase (PARP) inhibitors have attracted wide attention as a new class of anticancer drugs. PARP inhibitors act by blocking PARP binding to sites of DNA damage, leading to failure of DNA repair and ultimately leading to apoptosis of tumor cells. In recent years, PARP inhibitors have achieved remarkable clinical results in a variety of cancer treatments, especially in BRCA mutation-associated breast and ovarian cancer treatments. The application of these PARP inhibitor drugs has significantly prolonged the survival of patients associated with BRCA mutations and improved the quality of life of patients. The limitations of PARP inhibitors are mainly manifested in two aspects: first, their specificity for PARP-1 is not high enough to lead to side effects after taking drugs; second, patients may develop resistance during long-term use of such drugs. In this paper, the mechanism of action of PARP inhibitors is reviewed, and deeply analyzed the chemical structure and efficacy of Olaparib, Niraparib and Rucaparib drugs, which provided a useful reference for subsequent clinical research and drug development.

DbAMP 3.0: updated resource of antimicrobial activity and structural annotation of peptides in the post-pandemic era.

Antimicrobial resistance is one of the most urgent global health threats, especially in the post-pandemic era. Antimicrobial peptides (AMPs) offer a promising alternative to traditional antibiotics, driving growing interest in recent years. dbAMP is a comprehensive database offering extensive annotations on AMPs, including sequence information, functional activity data, physicochemical properties and structural annotations. In this update, dbAMP has curated data from over 5200 publications, encompassing 33,065 AMPs and 2453 antimicrobial proteins from 3534 organisms. Additionally, dbAMP utilizes ESMFold to determine the three-dimensional structures of AMPs, providing over 30,000 structural annotations that facilitate structure-based functional insights for clinical drug development. Furthermore, dbAMP employs molecular docking techniques, providing over 100 docked complexes that contribute useful insights into the potential mechanisms of AMPs. The toxicity and stability of AMPs are critical factors in assessing their potential as clinical drugs. The updated dbAMP introduced an efficient tool for evaluating the hemolytic toxicity and half-life of AMPs, alongside an AMP optimization platform for designing AMPs with high antimicrobial activity, reduced toxicity and increased stability. The updated dbAMP is freely accessible at https://awi.cuhk.edu.cn/dbAMP/. Overall, dbAMP represents a comprehensive and essential resource for AMP analysis and design, poised to advance antimicrobial strategies in the post-pandemic era.

Globalization in clinical drug development for sickle cell disease.

Globalization in clinical drug development for sickle cell disease.

Exploring the potential pharmacological mechanism of aripiprazole against hyperprolactinemia based on network pharmacology and molecular docking

The current primary therapeutic approach for schizophrenia is antipsychotic medication, and antipsychotic-induced hyperprolactinemia occurs in 40–80% of patients with schizophrenia. Aripiprazole, an atypical antipsychotic belonging to the quinolinone derivative class, can reduce the likelihood of developing hyperprolactinemia, but the pharmacological mechanisms of this reduction are unknown. This study aimed to explore the molecular mechanism of action of aripiprazole in treating hyperprolactinemia based on network pharmacology and molecular docking techniques. This study identified a total of 151 potential targets for aripiprazole from the DrugBank, TCMSP, BATMAN-TCM, TargetNet, and SwissTargetPrediction databases. Additionally, 71 hyperprolactinemia targets were obtained from the PharmGKB, DrugBank, TTD, GeneCards, OMIM, and DisGENET databases. Utilizing Venny 2.1.0 software, an intersection of 27 genes was identified between aripiprazole and hyperprolactinemia. To construct a common target protein–protein interaction (PPI) network, the common targets obtained from both sources were input into the STRING database. The resulting PPI network was then imported into Cytoscape 3.7.2 software, which identified eight core targets associated with aripiprazole’s treatment of hyperprolactinemia. Subsequently, a PPI network was established for these targets. Enrichment analysis of the key targets was conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in the DAVID database. Additionally, molecular docking verification of the interaction between aripiprazole and the core targets was performed using AutoDock Vina software. Aripiprazole’s intervention in hyperprolactinemia primarily targets the following core proteins: Solute Carrier Family 6 Member 3 (SLC6A3), monoamine oxidase (MAO-B), Dopamine D2 receptor (DRD2), 5-hydroxytryptamine (serotonin) receptor 2A (HTR2A), 5-hydroxytryptamine (serotonin) receptor 2C (HTR2C), cytochrome P450 2D6 (CYP2D6), Dopamine D1 receptor (DRD1), Dopamine D4 receptor (DRD4). These targets are predominantly involved in biological processes such as the adenylate cyclase-activating adrenergic receptor signaling pathway, G-protein coupled receptor signaling pathway coupled to cyclic nucleotide second messenger, phospholipase C-activating G-protein coupled receptor signaling pathway, chemical synaptic transmission, and response to xenobiotic stimulus. Primary enrichment occurs in signaling pathways such as the neuroactive ligand-receptor interaction and serotonergic synapse pathways. Molecular docking results demonstrate a favorable affinity between aripiprazole and the core target proteins MAO-B, DRD2, SLC6A3, HTR2C, HTR2A, CYP2D6, DRD4, and DRD1. Network pharmacology predicted potential targets and signaling pathways for aripiprazole’s intervention in hyperprolactinemia, offering theoretical support and a reference basis for optimizing clinical strategies and drug development involving aripiprazole.

Serum Glutamate dehydrogenase activity enables sensitive and specific diagnosis of hepatocellular injury in humans.

Serum activities of alanine- and aspartate- aminotransferases (ALT and AST) are considered the "gold standard" biomarkers of hepatocyte injury in clinical practice and drug development. However, due to expression of ALT and AST in myocytes, the diagnosis of hepatocellular injury in patients with underlying muscle diseases, including drug-induced muscle injury, is severely limited. Thus, we proposed glutamate dehydrogenase (GLDH) as a liver-specific alternative to serum ALT and AST. In fact, our exploratory studies showed that GLDH has comparable performance to ALT for detecting hepatocyte injury without interference from concomitant muscle injury. Here, we report the results of studies confirming the reference intervals in a healthy human population and sensitivity and specificity of GLDH for detection of hepatocyte injury in human subjects. In human subjects, we could not perform liver biopsies due to ethical reasons, we also confirmed the relationship of GLDH and histopathologic lesions using 32 model toxicants in rats. Furthermore, we have shown that injury to tissues that are known to express appreciable levels of GLDH does not affect serum GLDH measurements, indicating excellent liver specificity of serum GLDH. Finally, we observed faster elimination of GLDH than ALT in humans, indicating that decreasing GLDH values could be considered an early sign of recovery. This study provides comprehensive evidence of excellent sensitivity and liver specificity of GLDH for diagnosis of hepatocellular injury, including evaluation of reference intervals that is essential for interpretation of serum GLDH in human subjects.

AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.

Artificial intelligence (AI) is one of the fastest-growing fields in various industries, including engineering, architecture, medical and clinical research, aerospace, and others. AI, which is a combination of machine learning (ML), deep learning (DL), and human intelligence (HI), is revolutionizing drug discovery and development by making it more cost-effective and efficient. It is also being used in fields such as medicinal chemistry, molecular and cell biology, pharmacology, pharmacokinetics, formulation development, and toxicology. AI plays a crucial role in clinical testing by enhancing patient stratification, patient sample evaluation, and trial design, assisting in the identification of biomarkers, determining efficacy criteria, dose selection, trial length, and target patient population selection. The primary objective of this study is to emphasize the importance of AI in clinical trials and drug development, while also exploring the existing challenges and potential advancements in AI within the healthcare industry. A comprehensive literature review was conducted, covering the period from 1998 to 2023. The Science Direct, PubMed, and Google Scholar databases were searched for relevant information. A variety of publications, including Research Gate, Nature, MDPI, and Springer Link, provided pertinent data. This study aimed to gain a deeper understanding of the use of AI in clinical research and drug development, as well as its potential and limitations. We also discuss the benefits and main data limitations of the traditional trial and drug development approach. AI approaches are currently being used to overcome research obstacles and eliminate conceptual or methodological limitations. After discussing possible obstacles and coping mechanisms, we provide several recommendations to help individuals understand the challenges and difficulties associated with clinical research and drug development. It is essential for pharmaceutical companies to have a cutting-edge AI strategy if AI is to become a routine tool for clinical research and drug development.

Alzheimer’s disease: newer modalities in treatment

Alzheimer’s disease (AD) remains insurmountable despite a long history of research and clinical development. It is projected to rise in the coming decades of public health due to the aging population worldwide. Understanding the basic tenets of the disease and targeting key steps remains a time-tested approach even for AD. Apart from the currently available treatment options namely NMDA blockers, and acetylcholinesterase inhibitors, recently monoclonal antibodies that target Aβ amyloid protein are approved. This review highlights the key features of currently approved drugs along with promising drugs in clinical development.

Unraveling the Core Components and Critical Targets of Houttuynia cordata Thunb. in Treating Non-small Cell Lung Cancer through Network Pharmacology and Multi-omics Analysis.

This study aimed to preliminary explore the molecular mechanisms of Houttuynia cordata Thunb. (H. cordata; Saururaceae) in treating non-small cell lung cancer (NSCLC), with the goal of screening drug potential targets for clinical drug development. This study employed a multi-omics and multi-source data integration approach to identify potential therapeutic targets of H. cordata against NSCLC from the TCMSP database, GEO database, BioGPS database, Metascape database, and others. Meanwhile, target localization was performed, and its possible mechanisms of action were predicted. Furthermore, dynamics simulations and molecular docking were used for verification. Multiomics analysis was used to confirm the selected key genes' efficacy in treating NSCLC. A total of 31 potential therapeutic targets, 8 key genes, and 5 core components of H. cordata against NSCLC were screened out. These potential therapeutic targets played a therapeutic role mainly by regulating lipid and atherosclerosis, the TNF signaling pathway, the IL-17 signaling pathway, and others. Molecular docking indicated a stable combination between MMP9 and quercetin. Finally, through multi-omics analysis, it was found that the expression of some key genes was closely related not only to the progression and prognosis of NSCLC but also to the level of immune infiltration. Through comprehensive network pharmacology and multi-omics analysis, this study predicts that the core components of H. cordata play a role in treating NSCLC by regulating lipid and atherosclerosis, as well as the TNF signaling pathway. Among them, the anti-NSCLC activity of isoramanone is reported for the first time.

Disease coverage of human genome-wide association studies and pharmaceutical research and development

BackgroundDespite the growing interest in the use of human genomic data for drug target identification and validation, the extent to which the spectrum of human disease has been addressed by genome-wide association studies (GWAS), or by drug development, and the degree to which these efforts overlap remain unclear.MethodsIn this study we harmonize and integrate different data sources to create a sample space of all the human drug targets and diseases and identify points of convergence or divergence of GWAS and drug development efforts.ResultsWe show that only 612 of 11,158 diseases listed in Human Disease Ontology have an approved drug treatment in at least one region of the world. Of the 1414 diseases that are the subject of preclinical or clinical phase drug development, only 666 have been investigated in GWAS. Conversely, of the 1914 human diseases that have been the subject of GWAS, 1121 have yet to be investigated in drug development.ConclusionsWe produce target-disease indication lists to help the pharmaceutical industry to prioritize future drug development efforts based on genetic evidence, academia to prioritize future GWAS for diseases without effective treatments, and both sectors to harness genetic evidence to expand the indications for licensed drugs or to identify repurposing opportunities for clinical candidates that failed in their originally intended indication.

Integrating network pharmacology and transcriptomics to study the potential mechanism of Jingzhi Niuhuang Jiedu tablet in rats with accumulation of heat in the lungs and stomach

Ethnopharmacological relevanceAccumulation of heat in the lungs and stomach (AHLS) is an important syndrome within the realm of traditional Chinese medicine (TCM). It is the fundamental reason behind numerous illnesses, including mouth ulcers, dermatological conditions, acne, and pharyngitis. Jingzhi Niuhuang Jiedu tablet (JN) serves as the representative prescription for treatment of AHLS clinically. However, the effective components and mechanism of JN's impact on AHLS remain unexplored. Aim of the studyThe objective of this research was to analyze the effective components of JN and investigate the therapeutic effect and potential mechanism of JN on AHLS. Materials and methodsThe effective compounds of JN extract were analyzed and identified using UHPLC-Q-Exactive/HRMS. Utilizing network pharmacology to investigate JN's multi-target, multi-pathway process in treating AHLS. Subsequently, anti-inflammatory activities of JN extract were evaluated in the RAW264.7 cells stimulated by lipopolysaccharide (LPS). In addition, a rat AHLS model induced by LPS and dried ginger was established. Pathological changes in rat lung and stomach tissues observed by HE staining and Masson's trichrome staining. Additionally, the expression of TNF-α, IL-6, and IL-1β in bronchoalveolar lavage fluid (BALF) was identified through the ELISA assay. For a deeper understanding of how JN might affect AHLS, transcriptomics was utilized to examine differential genes and their underlying mechanisms. Concurrently, techniques like quantitative polymerase chain reaction (q-PCR), immunofluorescence, and western blotting (WB) were employed to confirm various mRNA and protein expression, including Il17ra, Il17re, IL-17A, IL-1β, IL-6, PPARγ, PGC1-α and UCP1. ResultsWe identified 178 potential effective components in the JN extract. Network pharmacology analysis showed that the 144 components in JN act on 200 key targets for the treatment of AHLS by suppressing inflammation, regulating energy metabolism, and gastric function. In addition, JN suppressed the LPS-stimulated generation of NO, TNF-α, IL-1β, and IL-6 in RAW264.7 cells. And JN treatment effectively alleviated lung and stomach injury and reduced inflammation in rats. Analysis of RNA-seq from lung tissues revealed JN's substantial control over crucial genes in the IL-17 signaling pathway, including Il1b and Il17ra. Likewise, RNA sequencing of stomach tissues revealed that JN markedly decreased crucial genes in the Thermogenesis pathway, including Ppargc1a and Ppara. Additional experimental findings confirmed that treatment with JN significantly reduced the expression levels of mRNA (Il17ra, Il17re, Il1b, Ppargc1a and Ucp1), and the expression levels of protein (IL-17A, IL-1β, IL-6, PPARγ, PGC1-α and UCP1). ConclusionThis study not only analyzes the effective components of JN but also reveals that JN could effectively ameliorate AHLS by inhibiting IL-17 signaling pathway and Thermogenesis pathway, which provides evidence for subsequent clinical studies and drug development.

Asia-inclusive drug development leveraging principles of ICH E5 and E17 guidelines: Case studies illustrating quantitative clinical pharmacology as a foundational enabler.

With the International Conference on Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) E17 guidelines in effect from 2018, the design of Asia-inclusive multiregional clinical trials (MRCTs) has been streamlined, thereby enabling efficient simultaneous global development. Furthermore, with the recent regulatory reforms in China and its drug administration joining the ICH as a full regulatory member, early participation of China in the global clinical development of novel investigational drugs is now feasible. This would also allow for inclusion of the region in the geographic footprint of pivotal MRCTs leveraging principles of the ICH E5 and E17. Herein, we describe recent case examples of model-informed Asia-inclusive global clinical development in the EMD Serono portfolio, as applied to the ataxia telangiectasia and Rad3-related inhibitors, tuvusertib and berzosertib (oncology), the toll-like receptor 7/8 antagonist, enpatoran (autoimmune diseases), the mesenchymal-epithelial transition factor inhibitor tepotinib (oncology), and the antimetabolite cladribine (neuroimmunological disease). Through these case studies, we illustrate pragmatic approaches to ethnic sensitivity assessments and the application of a model-informed drug development toolkit including population pharmacokinetic/pharmacodynamic modeling and pharmacometric disease progression modeling and simulation to enable early conduct of Asia-inclusive MRCTs. These examples demonstrate the value of a Totality of Evidence approach where every patient's data matter for de-risking ethnic sensitivity to inter-population variations in drug- and disease-related intrinsic and extrinsic factors, enabling inclusive global development strategies and timely evidence generation for characterizing benefit/risk of the proposed dosage in Asian populations.

Topological indices based VIKOR assisted multi-criteria decision technique for lung disorders.

Lung disorders involve swelling, inflammation, and muscle tightening around the airways, with symptoms such as coughing, wheezing, shortness of breath, and abnormal fluid build-up. The global prevalence of these conditions is rising, highlighting the need for extensive research to alleviate their severity and symptoms. Due to the chronic nature and recurrence of these disorders, the human body often develops immunity and side effects to certain medications. Therefore, developing novel and appropriate drug combinations is crucial. This study analyzes a dataset of lung disorder drugs, characterized by various topological indices. The structures of 16 drugs used to treat lung disorders are correlated with their physical properties using degree-based graph invariants. When considering specific attributes, the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method provides an optimal ranking for each drug. The QSPR results highlight the effectiveness of this approach in drug prioritization, offering valuable insights for clinical decision-making and drug development. This methodology can enhance the strategic selection of treatments for lung disorders, leading to improved patient care and better resource allocation.

Integrating network pharmacology and bioinformatics to explore the mechanism of Xiaojian Zhongtang in treating major depressive disorder: An observational study.

Major depressive disorder (MDD) is a common mental illness. The traditional Chinese medicine compound Xiaojian Zhongtang (XJZT) has a good therapeutic effect on MDD, but the specific mechanism is not clear. The aim of this study is to explore the molecular mechanism of XJZT in the treatment of MDD through network pharmacology and bioinformatics. The traditional Chinese medicine system pharmacology database was used to screen the chemical components and targets of XJZT, while the online Mendelian inheritance in man, DisGeNET, Genecards, and therapeutic target database databases were used to collect MDD targets and identify the intersection targets of XJZT and MDD. A "drugs-components-targets" network was constructed using the Cytoscape platform, and the STRING was used for protein-protein interaction analysis of intersecting targets. Gene Ontology and Kyoto encyclopedia of genes and genomes analysis of intersecting targets was performed using the DAVID database. Obtain serum and brain transcriptome datasets of MDD from the gene expression omnibus database, and perform differentially expressed genes, weighted gene co-expression network analysis, gene set enrichment analysis, and receiver operating characteristic analysis. A total of 127 chemical components and 767 targets were obtained from XJZT, among which quercetin, kaempferol, and maltose are the core chemical components, and 1728 MDD targets were screened out, with 77 intersecting targets between XJZT and MDD. These targets mainly involve AGE-RAGE signaling pathway in diabetic complexes, epidermal growth factor receptor tyrosine kinase inhibitor resistance, and HIF-1 signaling pathway, and these core targets have strong binding activity with core components. In addition, 1166 differentially expressed genes were identified in the MDD serum transcriptome dataset, and weighted gene co-expression network analysis identified the most relevant gene modules (1269 genes), among which RAC-alpha serine/threonine-protein kinase (AKT1), D(4) dopamine receptor (DRD4), and kynurenine 3-monooxygenase (KMO) were target genes for the treatment of MDD with XJZT, these 3 genes are mainly related to the ubiquitin-mediated proteolysis, arachidonic acid (AA) metabolism, and Huntington disease pathways, and the expression of AKT1, DRD4, and KMO was also found in the MDD brain transcriptome dataset, which is significantly correlated with the occurrence of MDD. We have identified 3 key targets for XJZT treatment of MDD, including AKT1, KMO, and DRD4, and they can be regulated by the key components of XJZT, including quercetin, maltose, and kaempferol. This provides valuable insights for the early clinical diagnosis and development of therapeutic drugs for MDD.

MiRNA375-3p/rapamycin mediates the mTOR pathway by decreasing PS1, enhances microglial cell activity to regulate autophagy in Alzheimer's disease

The clinical prevention, diagnosis, treatment, and drug development of Alzheimer's disease (AD) require urgent detection of novel targets and methods. Autophagy and microglia are significantly associated with the pathogenesis of early AD. This study indicated that microRNA-375-3p can inhibit autophagy by promoting mTOR phosphorylation in normal physiological conditions, while microRNA-375-3p promoted autophagy and enhanced neural repair by inhibiting the expression of presenilin 1 in early AD pathogenesis. Furthermore, co-treatment of rapamycin, and microRNA-375-3p can synergistically promote the autophagy and microglial activation in a neuroprotective manner, clear Aβ accumulation, repair nerve damage, and alleviate cognitive dysfunction and memory defects in APP/PS1 TG mice. This research revealed the impact and mechanism of miR375-3p on the early stage of AD through in vivo and in vitro experiments and provides new ideas and directions for the early treatment of AD.

In Silico clinical trial to predict the efficacy of hip protectors for preventing hip fractures

Osteoporosis is characterized by loss of bone mineral density and increased fracture risk. Reduction of hip fracture incidence is of major clinical importance. Hip protectors aim to attenuate the impact force transmitted to the femur upon falling, however different conclusions on their efficacy have been reported; some authors suggest this may be due to differences in compliance. The aim of this study was to apply an In Silico trial methodology to predict the effectiveness of hip protectors and its dependence on compliance.A cohort of 1044 virtual patients (Finite Element models of proximal femur) were generated. A Markov chain process was implemented to predict fracture incidence with and without hip protectors, by simulating different levels of compliance. At each simulated follow-up year, a Poisson distribution was randomly sampled to determine the number of falls sustained by each patient. Impact direction and force were stochastically sampled from a range of possible scenarios. The effect of wearing a hip protector was simulated by applying attenuation coefficients to the impact force (12.9 %, 19 % and 33.8 %, as reported for available devices). A patient was considered fractured when impact force exceeded the femur strength.Without hip protector, virtual patients experienced 66 ± 5 fractures in 10 years. Wearing the three devices, fracture incidence was reduced to 43 ± 4, 35 ± 4 and 17 ± 2 respectively, at full compliance. As expected, effectiveness was dependent on compliance.This In Silico trial technology can be applied in the future to test multiple interventions, optimise intervention strategies, improve clinical trial design and drug development.

Clinical Trial Findings and Drug Development Challenges for Curcumin in Infectious Disease Prevention and Treatment.

Since ancient times, turmeric, scientifically known as Curcuma longa, has been renowned for its therapeutic properties. Recently, extensive documentation has highlighted the prevalence of microbial diseases without effective treatments, the increased expense of certain antimicrobial medications, and the growing occurrence of antimicrobial drug resistance. Experts predict that drug resistance will emerge as a significant global cause of death by the middle of this century, thereby necessitating intervention. Curcumin, a major curcuminoid molecule, has shown extensive antimicrobial action. Improving and altering the use of natural antimicrobial agents is the most effective approach to addressing issues of targeted specificity and drug resistance in chemically synthesized medicines. Further research is required to explore the efficacy of curcumin and other natural antimicrobial substances in combating microbial infections. The solubility and bioavailability of curcumin impede its antimicrobial capability. To enhance curcumin's antimicrobial effectiveness, researchers have recently employed several methods, including the development of curcumin-based nanoformulations. This review seeks to compile the latest available literature to assess the advantages of curcumin as a natural antimicrobial agent (particularly antiviral and antibacterial) and strategies to enhance its medical efficacy. The future application of curcumin will help to alleviate microbial infections, thereby promoting the sustainability of the world's population.

Towards clinically relevant dose ratios for Cabamiquine and Pyronaridine combination using P. falciparum field isolate data

The selection and combination of dose regimens for antimalarials involve complex considerations including pharmacokinetic and pharmacodynamic interactions. In this study, we use immediate ex vivo P. falciparum field isolates to evaluate the effect of cabamiquine and pyronaridine as standalone treatments and in combination therapy. We feed the data into a pharmacometrics model to generate an interaction map and simulate meaningful clinical dose ratios. We demonstrate that the pharmacometrics model of parasite growth and killing provides a detailed description of parasite kinetics against cabamiquine-susceptible and resistant parasites. Pyronaridine monotherapy provides suboptimal killing rates at doses as high as 720 mg. In contrast, the combination of a single dose of 330 mg cabamiquine and 360 mg pyronaridine provides over 90% parasite killing in most of the simulated patients. The described methodology that combines a rapid, 3R-compliant in vitro method and modelling to set meaningful doses for new antimalarials could contribute to clinical drug development.

From organs to algorithms: Redefining cancer classification in the age of artificial intelligence.

Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect impacts clinical trials, drug development, and patient care. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for reclassifying cancers through comprehensive integration of molecular, histopathological, imaging, and clinical characteristics. AI-driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes.