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BACKGROUND: Low muscle mass (LMM) can be a frequent complication in Crohn's disease (CD). We attempted to explore the effect of LMM on the efficacy of biologics in patients with CD. METHODS: The retrospective cohort study included moderate-to-severe CD patients treated with infliximab or ustekinumab, and appendicitis patients as control. The skeletal muscle area (SMA) of L3 was assessed to evaluate the patients' muscle mass. After propensity score matching, the impact of LMM on drug efficacy was assessed in CD patients. RESULTS: A total of 269 patients with CD and 172 appendicitis patients were included. The CD group had lower skeletal muscle density and BMI, and a higher risk of developing LMM than the control group. BMI (OR = 0.48, p < 0.001) and previous use of biologics (OR = 2.94, p = 0.019) were found to be independently associated with LMM. LMM was found to be associated with a decrease in clinical response (at weeks 8-14), clinical remission (at weeks 8-14, 24-30 and 52) and biochemical remission (at week 52). At weeks 24-30 and 52, LMM was independently associated with loss of response (LOR). We found LMM could be a predictor of lower clinical remission at week 30, lower clinical remission at week 52 and a higher LOR rate at week 30 in infliximab. While in ustekinumab, LMM was associated with lower endoscopic remission at week 24, biochemical remission at week 52 and a higher LOR rate at weeks 24 and 52. CONCLUSIONS: The prevalence of LMM was higher in the CD group compared to the control group. For CD patients with LMM, the efficacy of infliximab and ustekinumab was relatively poor in both the short-term and long-term.

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The effectiveness of drug treatments is profoundly influenced by individual responses, which are shaped by gene expression variability, particularly within pharmacogenes. Leveraging single-cell RNA sequencing (scRNA-seq) data, our study explores the extent of expression variability among pharmacogenes in a wide array of cell types across eight different human tissues, shedding light on their impact on drug responses. Our findings broaden the established link between variability in pharmacogene expression and drug efficacy to encompass variability at the cellular level. Moreover, we unveil a promising approach to enhance drug efficacy prediction. This is achieved by leveraging a combination of cross-cell and cross-individual pharmacogene expression variation measurements. Our study opens avenues for more precise forecasting of drug performance, facilitating tailored and more effective treatments in the future.

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The triptans class of pharmaceuticals, which was created to treat acute migraine, is made up of indole-containing drugs that bind to a subset (1B/1D) of 5-hydroxytryptamine receptors and are agonists of serotonin receptors. At the moment, naratriptan, eletriptan, zolmitriptan, rizatriptan, almotriptan, and frovatriptan are the seven types of triptans available on the market. Among these are the FDA-approved triptans, Zolmitriptan and Sumatriptan, which are selective serotonin (5-hydroxytryptamine) agonists. Zolmitriptan, a synthetic tryptamine derivative and a well-known member of the triptan family, is available as an orally disintegrating tablet, nasal spray, and tablet. There are melt formulations of rizatriptan and zolmitriptan available on the market that are easier to use and absorb, comparable to regular pills. Recently, the FDA approved zolmitriptan, a medication with tolerability comparable to sumatriptan. Whereas zolmitriptan is only available as an oral melt or tablet, sumatriptan is available as a nasal spray, oral preparation, or self-injectable kit. The only known antimigraine drugs that were widely utilized before the triptan period were ergotamine and dihydroergotamine. However, zolmitriptan binds to plasma proteins only 25% of the time because of significant first-pass degradation. Researchers have looked into fresh ideas for solving this issue and innovations to overcome its pharmacokinetic difficulties. This article emphasizes the role of zolmitriptan in the treatment of migraines, highlighting its pharmacological properties, production, metabolism, and structural features.

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PURPOSE: To evaluate the efficacy and safety of current targeted drug therapies for radioiodine-refractory differentiated thyroid cancer (RR-DTC). METHODS: This was a meta-analysis of relevant randomized controlled trials (RCTs) and single-arm studies searched across PubMed, Embase, Cochranes, and Web of Sciences up to September 12, 2023. Stata15.0 software was used to assess overall survival (OS), progression-free survival (PFS), disease control rate (DCR), objective response rate (ORR), and adverse effects (AEs). The Cochrane Bias Risk tool was used to assess literature quality and trial bias and RevMan 5.4 was used to generate a quality assessment map. RESULTS: A total of 8 RCTs and 17 single-arm studies with 3,270 patients on 7 drugs-vandetanib, sorafenib, lenvatinib, cabozantinib, apatinib, donafenib, and anlotinib-were included. Targeted therapy with these drugs effectively prolonged PFS and OS in patients with RR-DTC with overall HRs of 0.35 (95% CI 0.23-0.53, P < 0.00001) and 0.53 (95% CI 0.32-0.86, P < 0.00001), respectively. ORR and DCR were also prolonged, with overall RRs of 27.63 (95% CI 12.39-61.61, P<0.00001) and 1.66 (95% CI 1.48-1.86, P<0.00001), respectively. The subgroup analysis using Effect Size (ES) showed that apatinib had the best effect on ORR with an ES of 0.66 (95% CI 0.49-0.83, P<0.00001) and DCR with a ES of 0.95 (95% CI 0.91-1.00, P<0.00001). Common drug adverse effects included hypertension, diarrhea, proteinuria, and fatigue. CONCLUSION: The currently used targeted drug therapies for RR-DTC can significantly improve clinical outcomes and the new drug apatinib demonstrates promise for potentially superior performance.

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Background: Personalized medicine has gained more attention for cancer precision treatment due to patient genetic heterogeneity in recent years. However, predicting the efficacy of antitumor drugs in advance remains a significant challenge to achieve this task. Objective: This study aims to predict the efficacy of antitumor drugs in individual cancer patients based on clinical data. Methods: This paper proposes to predict personalized antitumor drug efficacy based on clinical data. Specifically, we encode the clinical text of cancer patients as a probability distribution vector in hidden topics space using the Latent Dirichlet Allocation (LDA) model, named LDA representation. Then, a neural network is designed, and the LDA representation is input into the neural network to predict drug response in cancer patients treated with platinum drugs. To evaluate the effectiveness of the proposed method, we gathered and organized clinical records of lung and bowel cancer patients who underwent platinum-based treatment. The prediction performance is assessed using the following metrics: Precision, Recall, F1-score, Accuracy, and Area Under the ROC Curve (AUC). Results: The study analyzed a dataset of 958 patients with non-small cell cancer treated with antitumor drugs. The proposed method achieved a stratified 5-fold cross-validation average Precision of 0.81, Recall of 0.89, F1-score of 0.85, Accuracy of 0.77, and AUC of 0.81 for cisplatin efficacy prediction on the data, which most are better than those of previous methods. Of these, the AUC value is at least 4% higher than those of the previous. At the same time, the superior result over the previous method persisted on an independent dataset of 266 bowel cancer patients, showing the generalizability of the proposed method. These results demonstrate the potential value of precise tumor treatment in clinical practice. Conclusions: Combining LDA and neural networks can help predict the efficacy of antitumor drugs based on clinical text. Our approach outperforms previous methods in predicting drug clinical efficacy.

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Due to the pivotal role of carbonic anhydrase IX (CA IX) in pathological conditions, there's a pressing need for novel inhibitors to improve patient outcomes and clinical management. Herein, we investigated the inhibitory efficacy of six alkaloids from Ruta chalepensis against CA IX through in vitro inhibition assay and computational modeling. Skimmianine and maculosidine displayed significant inhibitory activity in vitro, with low IC50 values of 105.2 ± 3.2 and 295.7 ± 14.1 nM, respectively. Enzyme kinetics analyses revealed that skimmianine exhibited a mixed inhibition mode, contrasting with the noncompetitive inhibition mechanism observed for the reference drug (acetazolamide), as indicated by intersecting lines in the Lineweaver-Burk plots. The findings of docking calculations revealed that skimmianine and maculosidine exhibited extensive polar interactions with the enzyme. These alkaloids demonstrate substantial binding interactions and occupy identical binding site as acetazolamide, thereby enhancing their efficacy as inhibitors of CA IX. Utilizing a 100 ns molecular dynamics (MD) simulation, the dynamic interactions between isolated alkaloids and CA IX were intensively assessed. Analysis of diverse MD parameters revealed that skimmianine and maculosidine displayed consistent trajectories and notable energy stabilization during their interaction with CA IX. The findings of MM/PBSA analysis depicted the minimum binding free energy for skimmianine and maculosidine. In addition, the Potential Energy Landscape (PEL) analysis revealed distinct and stable conformational states for the CA IX-ligand complexes, with Skimmianine showing the most stable and lowest energy configuration. These computational findings align with experimental results, emphasizing the potential efficacy of skimmianine and maculosidine as inhibitors of CA IX.

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Introduction Tuberculosis (TB) infection continues to be a major chronic infection causing significant morbidity and mortality, despite being a preventable and treatable infectious disease. The emergence and rapid spread of drug-resistant strains of Mycobacterium tuberculosis (MTB), the causative bacteria, present a formidable challenge to global TB control efforts. This study aimed to estimate the efficacy of TB treatment regimens and their successful outcomes in a retrospective analysis carried out in a tertiary health care hospital. Materials and methods A retrospective analysis was carried out on the patients diagnosed with TB and treated with different treatment regimens at Saveetha Medical College and Hospital (SMCH), Chennai, India, between November 2022 and July 2023. Data were collected on demographics, clinical characteristics, treatment regimens, and treatment outcomes of the above patients. Results A total of 234 patients were included in the study. The patients were divided according to sex, age, and resistant characteristics; the statistical significance of the collected population was determined. Treatment regimens were followed as either a six-month regimen or nine-month regimen. Conclusion This study provides insights into the comparative efficacy of two TB treatment regimens. The findings highlight the importance of proper analysis of the resistance status of the drug and the initiation of medication over an appropriate period of time.

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BACKGROUND: Nonalcoholic Steatohepatitis (NASH) results from complex liver conditions involving metabolic, inflammatory, and fibrogenic processes. Despite its burden, there has been a lack of any approved food-and-drug administration therapy up till now. PURPOSE: Utilizing machine learning (ML) algorithms, the study aims to identify reliable potential genes to accurately predict the treatment response in the NASH animal model using biochemical and molecular markers retrieved using bioinformatics techniques. METHODS: The NASH-induced rat models were administered various microbiome-targeted therapies and herbal drugs for 12 weeks, these drugs resulted in reducing hepatic lipid accumulation, liver inflammation, and histopathological changes. The ML model was trained and tested based on the Histopathological NASH score (HPS); while (0-4) HPS considered Improved NASH and (5-8) considered non-improved, confirmed through rats' liver histopathological examination, incorporates 34 features comprising 20 molecular markers (mRNAs-microRNAs-Long non-coding-RNAs) and 14 biochemical markers that are highly enriched in NASH pathogenesis. Six different ML models were used in the proposed model for the prediction of NASH improvement, with Gradient Boosting demonstrating the highest accuracy of 98% in predicting NASH drug response. FINDINGS: Following a gradual reduction in features, the outcomes demonstrated superior performance when employing the Random Forest classifier, yielding an accuracy of 98.4%. The principal selected molecular features included YAP1, LATS1, NF2, SRD5A3-AS1, FOXA2, TEAD2, miR-650, MMP14, ITGB1, and miR-6881-5P, while the biochemical markers comprised triglycerides (TG), ALT, ALP, total bilirubin (T. Bilirubin), alpha-fetoprotein (AFP), and low-density lipoprotein cholesterol (LDL-C). CONCLUSION: This study introduced an ML model incorporating 16 noninvasive features, including molecular and biochemical signatures, which achieved high performance and accuracy in detecting NASH improvement. This model could potentially be used as diagnostic tools and to identify target therapies.

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Modelos Animales de Enfermedad , Aprendizaje Automático , Enfermedad del Hígado Graso no Alcohólico , Animales , Enfermedad del Hígado Graso no Alcohólico/tratamiento farmacológico , Enfermedad del Hígado Graso no Alcohólico/genética , Enfermedad del Hígado Graso no Alcohólico/patología , Ratas , Hígado/patología , Hígado/metabolismo , Hígado/efectos de los fármacos , Masculino , Proteínas Señalizadoras YAP/genética , Biomarcadores/sangre , MicroARNs/genética

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Purpose: This article illustrates the replication of asthma and COPD conditions in a laboratory setting and the potential applications of this methodology.Introduction: Biologic drugs have been shown to enhance the treatment of severe asthma and COPD. Monoclonal antibodies against specific targets have dramatically changed the management of these conditions. Although the inflammatory pathways of asthma and COPD have already been clearly outlined, alternative mechanisms of action remain mostly unexplored. They could provide additional insights into these diseases and their clinical management.Aims: In vivo or in vitro models have thus been developed to test alternative hypotheses. This study describes sophisticated ex vivo models that mimic the response of human respiratory mucosa to disease triggers, aiming to narrow the gap between laboratory studies and clinical practice.Results: These models successfully replicate crucial aspects of these diseases, such as inflammatory cell presence, cytokine production, and changes in tissue structure, offering a dynamic platform for investigating disease processes and evaluating potential treatments, such as monoclonal antibodies. The proposed models have the potential to enhance personalized medicine approaches and patient-specific treatments, helping to advance the understanding and management of respiratory diseases.

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Alopecia areata is the second most common form of hair loss in humans after androgenetic alopecia. Although a variety of animal models for alopecia areata have been described, currently the C3H/HeJ mouse model is the most commonly used and accepted. Spontaneous hair loss occurs in 15%-25% of older mice in which the lesions wax and wane, similar to the human disease, with alopecia being more common and severe in female mice. Full-thickness skin grafts from mice with spontaneous alopecia areata to young, normal-haired, histocompatible mice provide a highly reproducible model with progressive lesions that makes it useful for drug efficacy and mechanism-based studies. As alopecia areata is a cell-mediated autoimmune disease, transfer of cultured lymph node cells from affected mice to unaffected, histocompatible recipients also promotes disease development and provides an alternative, nonsurgical protocol. Protocols are presented to produce these models such that they can be used to study alopecia areata and to develop novel drug therapies. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Full-thickness skin grafts to reproducibly induce alopecia areata in C3H/HeJ mice Basic Protocol 2: Adoptive transfer of cultured lymphoid cells provides a nonsurgical method to induce alopecia areata in C3H/HeJ mice.

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Alopecia Areata , Modelos Animales de Enfermedad , Ratones Endogámicos C3H , Trasplante de Piel , Alopecia Areata/tratamiento farmacológico , Alopecia Areata/patología , Alopecia Areata/inmunología , Animales , Ratones , Femenino , Masculino , Traslado Adoptivo

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Investigating disease progression, transmission of infection and impacts of Multidrug Therapy (MDT) to inhibit demyelination in leprosy involves a certain amount of difficulty in terms of the in-built uncertain complicated and complex intracellular cell dynamical interactions. To tackle this scenario and to elucidate a more realistic, rationalistic approach of examining the infection mechanism and associated drug therapeutic interventions, we propose a four-dimensional ordinary differential equation-based model. Stochastic processes has been employed on this deterministic system by formulating the Kolmogorov forward equation introducing a transition state and the quasi-stationary distribution, exact distribution analysis have been investigated which allow us to estimate an expected time to extinction of the infected Schwann cells into the human body more prominently. Additionally, to explore the impact of uncertainty in the key intracellular factors, the stochastic system is investigated incorporating random perturbations and environmental noises in the disease dissemination, proliferation and reinfection rates. Rigorous numerical simulations validating the analytical outcomes provide us significant novel insights on the progression of leprosy and unravelling the existing major treatment complexities. Analytical experiments along with the simulations utilizing Monte-Carlo method and Euler-Maruyama scheme involving stochasticity predicts that the bacterial density is underestimated due to the recurrence of infection and suggests that maintaining a drug-efficacy rate in the range 0.6-0.8 would be substantially efficacious in eradicating leprosy.

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Lepra , Modelos Biológicos , Células de Schwann , Procesos Estocásticos , Humanos , Células de Schwann/microbiología , Lepra/microbiología , Lepra/tratamiento farmacológico , Conceptos Matemáticos , Método de Montecarlo , Mycobacterium leprae

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OBJECTIVES: To assess the reporting of meta-analysis abstracts on drug efficacy for tumors in terms of adherence to Preferred Reporting Items for Systematic Reviews and Meta-analyses for Abstracts (PRISMA-A) and identify the potential factors associated with adherence to PRISMA-A. STUDY DESIGN AND SETTING: A total of 3,211 eligible meta-analysis abstracts were assessed using a checklist adapted from the PRISMA-A statement. Adherence to PRISMA-A was analyzed by the total PRISMA-A score and adherence rate (AR). The independent samples t-test was performed to compare the difference of the total scores between two groups with different characteristics, and the analysis of variance or Kruskal-Wallis test was used among multiple groups. The Pearson's correlation coefficient was used to measure the correlation between the word count and the total PRISMA-A score. RESULTS: The mean total score was 8.11 (±1.76) and the AR was 57.94%. The items with lower AR were funding (AR = 0.93%), registration (AR = 3.86%), and risk of bias (AR = 7.85%). Meta-analyses published after the release of PRISMA-A showed better adherence to PRISMA-A. Compared to unstructured abstracts, structured abstracts had a higher AR for each item in PRISMA-A. There was a positive correlation between the word count of abstract and the total PRISMA-A score (r = 0.358, P < .001). CONCLUSION: Adherence to PRISMA-A was suboptimal in meta-analysis abstracts on drug efficacy for tumors, despite the improvement after the release of PRISMA-A. Various measures should be implemented to improve compliance with PRISMA-A and enhance the reporting of meta-analysis abstracts, including journal endorsement of PRISMA-A, requirement of stricter adherence to PRISMA-A, relaxation of abstract word limits, etc. PLAIN LANGUAGE SUMMARY: Meta-analysis is the statistical method used to compare and synthesize the results of studies on the same result research problem. It is integral in guiding evidence-based decision making in clinical practice. However, crucial information is frequently inadequately documented in meta-analysis abstracts, thereby reducing their significance for readers. And there has been a lack of published research evaluating the reporting of meta-analysis abstracts in the field of drug efficacy for tumors. The objectives of our study were (1) to assess the reporting of meta-analysis abstracts on drug efficacy for tumors in terms of adherence to Preferred Reporting Items for Systematic Reviews and Meta-analyses for Abstracts (PRISMA-A); and (2) to identify factors that might influence adherence to PRISMA-A. Our study reveals that meta-analyses published after the release of PRISMA-A showed better adherence to PRISMA-A, although there is still large room for improvement. Compared to unstructured abstracts, structured abstracts received the higher adherence rate (AR) for each item in PRISMA-A. There was a positive correlation between the word counts of abstract and the total PRISMA-A scores. Our study suggests that more efforts are still needed to improve the adherence to PRISMA-A in meta-analysis abstracts on drug efficacy for tumors. The journal editors should endorse PRISMA-A to authors, appropriately relax the word limit for abstracts, and provide authors with the writing template for structured abstracts.

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BACKGROUND: Hepatocellular carcinoma (HCC) is a highly aggressive cancer with limited therapeutic options. Hypoxia is a common feature of the tumor microenvironment that reportedly promotes tumorigenesis. Long non-coding RNAs (lncRNAs) are a class of regulatory molecules with diverse functions in cancer biology. This study aimed to identify hypoxia-induced lncRNAs associated with HCC and evaluate their potential as prognostic and therapeutic biomarkers. METHODS: We employed microarray and The Cancer Genome Atlas (TCGA) data to identify hypoxia-induced lncRNAs in HCC. Subsequently, we focused on CTD-2510F5.4, a candidate lncRNA, and predicted its functional roles in HCC using Gene Ontology (GO) and Guilt-by-Association (GBA) analyses. We validated its expression under hypoxia in Huh7 and HepG2 cells using RT-PCR. Functional assays, including CCK8, wound-healing, and transwell assays, were performed to assess the effects of CTD-2510F5.4 overexpression on HCC cell proliferation, invasion, and metastasis potential. Furthermore, we investigated the association between CTD-2510F5.4 expression and patient prognosis, tumor mutation signature, immune microenvironment characteristics, and therapeutic response to different treatment modalities. RESULTS: Our data demonstrated a significant upregulation of CTD-2510F5.4 expression in response to hypoxia. Functional enrichment analyses revealed the involvement of CTD-2510F5.4 in cell cycle regulation, E2F targets, G2M checkpoint control, and MYC signaling pathways. Functionally, CTD-2510F5.4 overexpression promoted HCC cell proliferation, invasion, and metastasis. Patients with high CTD-2510F5.4 expression exhibited a worse prognosis, a higher prevalence of TP53 mutations, increased infiltration by immunosuppressive regulatory T cells, elevated expression of immune checkpoint molecules, and higher TIDE scores indicative of immune dysfunction and exclusion. Notably, patients with low CTD-2510F5.4 expression displayed greater sensitivity to immunotherapy and antiangiogenic therapy, while those with high expression responded better to chemotherapy. CONCLUSION: Our findings suggest that CTD-2510F5.4 plays a critical role in HCC progression and immune modulation. Its potential as a prognostic biomarker and a predictor of therapeutic response warrants further investigation for personalized treatment strategies in HCC patients.

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Biomarcadores de Tumor , Carcinoma Hepatocelular , Proliferación Celular , Regulación Neoplásica de la Expresión Génica , Neoplasias Hepáticas , ARN Largo no Codificante , Microambiente Tumoral , Humanos , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/patología , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/patología , Microambiente Tumoral/genética , ARN Largo no Codificante/genética , Pronóstico , Proliferación Celular/genética , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Línea Celular Tumoral , Células Hep G2 , Hipoxia/genética , Movimiento Celular/genética

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Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce "efficacious combination benefit" (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers in vitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.

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Protocolos de Quimioterapia Combinada Antineoplásica , Sinergismo Farmacológico , Neoplasias , Humanos , Línea Celular Tumoral , Animales , Neoplasias/tratamiento farmacológico , Neoplasias/patología , Protocolos de Quimioterapia Combinada Antineoplásica/farmacología , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapéutico , Ensayos de Selección de Medicamentos Antitumorales/métodos , Ensayos Antitumor por Modelo de Xenoinjerto , Ratones , Antineoplásicos/farmacología , Antineoplásicos/uso terapéutico , Biomarcadores de Tumor/metabolismo

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Controlled drug delivery technology has matured for more than 70 years, starting from a twice-a-day oral formulation to 6 month long-acting injectable formulations. Further technological advances require superior formulations to treat various diseases more efficiently. Developing future formulations with practical innovations for treating existing and new diseases necessitates our continued efforts to overcome at least three main hurdles. They include (i) drug delivery with reduced side effects, (ii) long-term treatment of chronic diseases, and (iii) the overcoming of biological barriers. Such efforts start with the improved ability to accurately test drug delivery efficacy using proper controls. Future development can be aided by artificial intelligence if used properly. The next revolution of drug delivery systems will be augmented if implementation is given equal weight as discovery. Such a process can be accelerated with the systemic revamp of the research funding structure and cultivating a new generation of scientists who can think differently.

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Sistemas de Liberación de Medicamentos , Sistemas de Liberación de Medicamentos/métodos , Humanos , Preparaciones de Acción Retardada , Inteligencia Artificial

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BACKGROUND: Plasmodium vivax malaria is still an important public health problem in Ethiopia. Unlike Plasmodium falciparum, P. vivax has a dormant liver stage (hypnozoite) that can be a risk of recurrent vivax malaria unless treated by radical cure with primaquine. Drug resistance to chloroquine is threatening malaria control and elimination efforts. This study assessed the therapeutic efficacy and safety of chloroquine plus 14 days of primaquine on P. vivax infection based on parasitological, clinical, and haematological parameters. METHODS: A single-arm in vivo prospective therapeutic efficacy study was conducted to assess the clinical and parasitological response to the first-line treatment of P. vivax in Ethiopia, chloroquine plus 14 days low dose of (0.25 mg/kg/day) primaquine between December 2022 and March 2023 at Hamusit Health Centre using the standard World Health Organization (WHO) protocol. A total of 100 study participants with P. vivax mono-infection who were over 6 months old were enrolled and monitored for adequate clinical and parasitological responses for 42 days. The WHO double-entry Excel sheet and SPSS v.25 software were used for Kaplan-Meier survival analysis, and a paired t-test was used for analysis of haemoglobin improvements between follow up days. RESULTS: A total of 100 patients were enrolled among those, 96% cases were rural residents, 93% had previous malaria exposure, and predominant age group was 5-15 years (61%). 92.6% (95% CI 85.1-96.4%) of enrolled patients were adequate clinical and parasitological response, and 7.4% (95% CI 3.6-14.9%) recurrences were observed among treated patients. The fever and parasite clearance rate on day 3 were 98% and 94%, respectively. The baseline haemoglobin levels improved significantly compared to those days 14 and 42 (p < 0.001). No serious adverse event was observed during the study period. CONCLUSIONS: In this study, co-administration of chloroquine with primaquine was efficacious and well-tolerated with fast resolution of fever and high parasites clearance rate. However, the 7.4% failure is reported is alarming that warrant further monitoring of the therapeutic efficacy study of P. vivax.

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Antimaláricos , Cloroquina , Quimioterapia Combinada , Malaria Vivax , Plasmodium vivax , Primaquina , Malaria Vivax/tratamiento farmacológico , Cloroquina/uso terapéutico , Cloroquina/administración & dosificación , Cloroquina/efectos adversos , Primaquina/uso terapéutico , Primaquina/administración & dosificación , Etiopía , Antimaláricos/uso terapéutico , Antimaláricos/administración & dosificación , Antimaláricos/efectos adversos , Humanos , Adolescente , Masculino , Adulto , Adulto Joven , Femenino , Niño , Estudios Prospectivos , Persona de Mediana Edad , Preescolar , Plasmodium vivax/efectos de los fármacos , Anciano

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To investigate predictive biomarkers that could be used to identify patients' response to treatment, plasma metabolomics and proteomics analyses were performed in Kashin-Beck disease (KBD) patients treated with Fufang Duzhong Jiangu Granules (FDJG). Plasma was collected from 12 KBD patients before treatment and 1 month after FDJG treatment. LC-MS and olink proteomics were employed for obtaining plasma metabolomics profiling and inflammatory protein profiles. Patients were classified into responders and non-responders based on drug efficacy. Enrichment analyses of differential metabolites and proteins of the responders at baseline and after treatment were conducted to study the mechanism of drug action. Differential metabolites and proteins between the two groups were screened as biomarkers to predict the drug efficacy. The receiver operating characteristic curve was used to evaluate the prediction accuracy of biomarkers. The changes in metabolites and inflammatory proteins in responders after treatment reflected the mechanism of FDJG treatment for KBD, which may act on glycerophospholipid metabolism, d-glutamine and d-glutamate metabolism, nitrogen metabolism and NF-kappa B signaling pathway. Three metabolites were identified as potential predictors: N-undecanoylglycine, ß-aminopropionitrile and PC [18:3(6Z,9Z,12Z)/20:4(8Z,11Z,14Z,17Z)]. For inflammatory protein, interleukin-8 was identified as a predictive biomarker to detect responders. Combined use of these four biomarkers had high predictive ability (area under the curve = 0.972).

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Biomarcadores , Medicamentos Herbarios Chinos , Enfermedad de Kashin-Beck , Metabolómica , Humanos , Enfermedad de Kashin-Beck/sangre , Enfermedad de Kashin-Beck/tratamiento farmacológico , Masculino , Medicamentos Herbarios Chinos/uso terapéutico , Femenino , Persona de Mediana Edad , Biomarcadores/sangre , Metabolómica/métodos , Proteómica/métodos , Metaboloma/efectos de los fármacos , Adulto , Proteínas Sanguíneas/metabolismo , Proteínas Sanguíneas/análisis , Resultado del Tratamiento , Cromatografía Liquida/métodos

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BACKGROUND: Osteosarcoma (OS) usually happens in patients under 20 years old and is notorious for its low survivorship and limb loss. Personalized medicine is a viable approach to increase the efficiency of chemotherapy which is the main prognostic factor for survivorship after surgical treatment. METHODS: In this five-year prospective observational study, we collected primary cells of osteosarcoma from 15 patients, and examined the correlation between clinical characters of patients and cell properties characterized using various in vitro assays. The properties including genes expression, pro-angiogenic capability and anti-cancer drug response are characterized respectively by using RT-PCR, tube formation assay, osteogenesis assay and drug testing on 3D tumor spheroid model. RESULT: The results suggest that OS patients with higher MMP9 expression levels have higher probability to develop skip metastasis (p = 0.041). The 3D tumor spheroid test based on the median lethal dose from 2D culture provides some prognostic value. Patients do not response well to methotrexate (MTX) show higher percentage of high pathology grade (p = 0.009) and lung metastasis (p = 0.044). Also, patients respond well to ifosfamide (IFO) have higher probability to achieve high tumor necrosis rate (p = 0.007). CONCLUSION: The association between cell properties and clinical characters of patients provided by our data can act as potential prognostic factors to help physicians to develop effective personalized chemotherapy for osteosarcoma treatments.

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In order to accelerate drug development and avoid unnecessary drug trials in vulnerable pediatric populations, the US Food and Drug Administration (FDA) released a general advice letter to sponsors permitting the effectiveness of atypical antipsychotics for the treatment of schizophrenia in adults to be extrapolated to adolescents. Extrapolation is based on the evidence-based assumptions that (1) disease characteristics and (2) response to therapy, are similar in adults and adolescents. Whereas the FDA validated the extrapolation approach using data from multiple drug development programs, aripiprazole data are the most relevant to confirm the validity of the extrapolation approach for brexpiprazole, since aripiprazole and brexpiprazole both modulate dopaminergic and serotonergic signaling in the brain. The aims of this analysis were (1) to quantitatively assess the aripiprazole exposure (average steady-state concentration)-response (Positive and Negative Syndrome Scale total score change from baseline) similarity between adults and adolescents with schizophrenia, (2) to extend the aripiprazole exposure-response modeling to brexpiprazole using adult data, and (3) to use the brexpiprazole model to predict schizophrenia symptom response in adolescents. Disease-drug-dropout models were developed using patient-level data from clinical studies of aripiprazole (1007 adults, 294 adolescents) and brexpiprazole (1235 adults) in schizophrenia. The aripiprazole model demonstrated similar exposure-response between adults and adolescents with schizophrenia, validating the extrapolation approach. Extrapolation of the brexpiprazole adult exposure-response model to adolescents predicted the efficacy of brexpiprazole in adolescents aged 13-17 years with schizophrenia.