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Nontuberculous mycobacteria (NTM) are increasingly recognized as major causes of pulmonary disease worldwide. However, progress in identifying effective clinical treatments is difficult because standardized, high-burden preclinical models that enable rapid quantitative classification and comparison of drug performance in slow-growing mycobacteria (SGM) and rapid-growing mycobacteria (RGM) species are lacking. This study describes a framework for benchmarking treatment efficacy using NTM species-host matched infection models of Mycobacterium avium 2285 in immunocompetent C57BL/6 mice and Mycobacterium abscessus ATCC 19977 in immunodeficient NOD. CB17-Prkdcscid/NCrCrl (NOD-SCID) mice. A consistent high-burden respiratory infection is established by real-time quantification of viable inoculum, ensuring reproducible bacterial lung burden across experiments. The short-course treatment duration provides a rapid and resource-efficient therapeutic window for assessing pharmacological response. Analytical outputs integrate absolute CFU reduction with variance-adjusted effect size (Hedges’ g ), categorical efficacy classification, and an MIC-Adjusted Clearance Index to generate potency-normalized measures of efficacy in these models. Performance was validated using a reference panel of antimicrobials representing diverse drug classes and mechanisms of action, including macrolides, rifamycins, fluoroquinolones, and diarylquinolines, to ensure broad benchmarking across pharmacological targets. The framework revealed consistent NTM species-specific patterns of drug performance, with higher potency-adjusted efficacy in M. avium than in M. abscessus , consistent with known clinical behavior. Together, these data establish a reproducible and standardized preclinical platform for early efficacy evaluation, enabling rapid, quantitative benchmarking across standardized RGM and SGM infection models, improving the translational predictability of NTM drug development.

Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterogeneity network, where Meta-paths contain all the shortest paths between drug targets and disease-related proteins in the network and drug efficacy is measured by a predictive score according to drug disease network proximity. Experiments show that Meta-DEP performs better than traditional network topology analysis on drug-disease interaction prediction task. Further investigations demonstrate that the key targets identified by Meta-DEP for drug efficacy are consistent with clinical pharmacological evidence. To prove that Meta-DEP can be used to discover active natural compounds, we apply it to predict the relationship between the monomeric components of traditional Chinese medicine included in the TCMSP database and diseases. Results indicate that Meta-DEP can accurately predict most of the drug-disease pairs included in the TCMSP database. In addition, biological experiments are directly used to demonstrate that Meta-DEP can mined active compound from traditional Chinese medicine with integrating disease transcriptomic data. Overall, the model developed in this study provides new impetus for driving the natural compound into innovative lead molecule. Code and data are available at https://github.com/t9lex/Meta-DEP .

Due to differences in physiological characteristics and drug metabolism between children and adults, drug efficacy evaluation and safety monitoring in pediatric drug development present significant challenges. This paper proposes a data-driven incentive mechanism for pediatric drug development based on medical imaging data. This approach optimizes drug market pricing through precise imaging data, promoting accessibility and R&D efficiency for pediatric drugs. This study first collects multi-source computed tomography (CT), magnetic resonance imaging (MRI), and X-ray data, focusing on images of common pediatric diseases. After data preprocessing, a convolutional neural network (CNN) is used for feature extraction to extract key image information. Image difference methods and a U-Net image segmentation network are then used to evaluate drug efficacy and safety, quantify efficacy changes, and analyze side effects. Next, a drug efficacy-safety evaluation model is developed, and game theory is employed to design a R&D incentive mechanism. Monte Carlo simulation is combined with risk assessment to comprehensively consider factors such as cost, R&D investment, and market demand during the pricing optimization phase. A dynamic pricing strategy is implemented to ensure both economic benefits and social accessibility of the drug. Experiments have shown that the drug has a good development effect, with an average tumor volume reduction of 32.7% (95% CI: 28.4%-36.9%). The drug’s impact on organ volume is within ± 2 cm³, and the market pricing strategy selects a relatively optimal price point.

Background: The multiple drug-resistant phenomenon has long since plagued the effectiveness of various chemotherapies used in the treatment of patients with glioblastoma (GBM), which is still incurable to this day. ATP-binding cassette (ABC) transporters function as drug transporters and have been touted to be the main culprits in developing resistance to xenobiotic drugs in GBM. Methods: This review systematically analyzed the efficacy of ABC transporters against various anticancer drugs from 16 studies identified from five databases (PubMed, Medline, Embase, Scopus, and ScienceDirect). Results: Inhibition of ABC transporters, especially ABCB1, improved drug efficacies. Staple GBM phenotypes, such as GBM stem cells and increased activation of the PI3K/Akt/NF-κB pathway, have been implicated in the expression of several ABC transporters. Using the datasets in The Cancer Genome Atlas and Gene Expression Omnibus, we found upregulated ABC transporters that either negatively impacted survival in univariate analyses (ABCA1, ABCA13, ABCB9, ABCD4) or were independent negative prognosis factors for patients with GBM (ABCA13, ABCB9). Our multivariate analysis further demonstrated three ABC transporters, ABCA13 (Hazard Ratio (HR) = 1.31, p = 0.017), ABCB9 (HR = 1.26, p = 0.03), and ABCB5 (HR = 0.77, p = 0.016), with the administration of alkylating agents (HR = 0.41, p < 0.001), were independent negative prognosis factors for patients with GBM. Conclusions: These findings reinforce the important role played by ABC transporters, particularly by ABCA13, ABCB9, and ABCB1, which could be potential targets that warrant further evaluations for alternate strategies to augment the effects of existing alkylating agents and xenobiotic drugs.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy associated with rapid metastasis and chemoresistance driven by PI3K/Akt/Nrf2 signalling and drug efflux transporters. The lack of symptoms and early diagnosis are clinically challenging, and the development of new medications is limited. Therefore, a new strategy to enhance gemcitabine efficacy without increasing systemic toxicity has been demonstrated. The fragment-based drug sensitiser BD B10 was selected from a Maybridge fragment library using the Tanimoto coefficient to identify structural similarity to trigonelline and tryptamine. PDAC cell lines and non-cancerous pancreatic cells were reated with gemcitabine, BD B10, or their combination. Cell viability, apoptosis, migration, and signalling pathways were analysed using microscopy, flow cytometry, RT-qPCR, Western blot, and RNA Seq with pathway analysis. Applying BD B10 in PDAC cell lines reduced the dose requirement of gemcitabine by 10%, with no adverse effects on growth of non-cancerous pancreatic cell lines, enhancing drug efficacy by 12%, with a otential marked gain in therapeutic index. Additionally, combination treatment enhanced apoptosis, reduced migration, and impeding PI3K/Akt/Nrf2, STAT3, and Wnt/β-catenin signalling regulation. BD B10 was identified as a non-toxic drug sensitiser that enhanced gemcitabine efficacy in PDAC cells and improved the therapeutic index by inhibiting key survival and resistance pathways. Specific roles for BD B10 in PDAC were identified and further testing may prove drug sensitisers have a more general application to enhance drug therapies.

BackgroundCell spheroids and aggregates generated from three-dimensional (3D) cell culture methods are similar to in vivo tumors in terms of tissue morphology, biology, and gene expression, unlike cells grown in 2D cell cultures. Breast cancer heterogeneity is one of the main drug resistant mechanisms and needs to be overcome in order to increase the efficacy of drug activity in its treatments.MethodsWe performed a unique 3D cell culture and drug efficacy study with trastuzumab emtansine (Kadcyla®, T-DM1) across five breast cancer cell lines (BT-474, SK-BR-3, MDA-MB-361, MDA-MB-175, and MCF-7) that were previously investigated in 2D cell culture. We performed HER2 IHC staining, cell viability experiments, Gene-protein-assay (GPA), and T-DM1 internalization studies.ResultsWe obtained significantly different results including higher IC50 for some of the cell lines. Our GPA showed some significant heterogeneous HER2 gene and protein expression in 3D cultured spheroids or aggregates. The fluorescent images also showed that a longer incubation time is needed for T-DM1 to be internalized effectively into 3D cultured spheroids or aggregates.ConclusionOur study demonstrated that the difference of T-DM1 drug activity in 3D spheroids or aggregates might be due to tumor heterogeneity and less efficient internalization of T-DM1 that is not seen using 2D cell culture models. Drug studies using 3D cell culture are expected to provide biologically relevant models for determining drug activity in tumor tissue in future drug response and resistance research.

Background Due to the complexity of tumor genetic heterogeneity, personalized medicine has progressively emerged as the central focus of cancer research. However, how to accurately predict the drug response of patients before receiving treatment is the critical challenge to the development of this field. Methods This paper proposes DrugBERT, a BERT-based framework integrated with LDA topic embedding and a drug efficacy-aware mechanism for predicting the efficacy of antitumor drugs. The method incorporates LDA-generated topic embedding as a semantic enhancement module into the BERT language model and introduces a drug efficacy-aware attention mechanism to prioritize drug efficacy-related semantic features. The model is via LSTM to capture long-range dependencies in clinical text data. In addition, the SMOTE algorithm is used to synthesize samples of the minority class to solve the problem of data imbalance. Results The proposed method DrugBERT demonstrated remarkable performance on a dataset of 958 patients with non-small cell cancer treated with antitumor drugs. Furthermore, when validated on an independent dataset of 266 bowel cancer patients, the model achieved a 3% improvement in AUC over previous methods, signifying its robust generalization capability. Conclusions DrugBERT can help predict the efficacy of antitumor drugs based on clinical text while exhibiting strong generalization capability. These findings highlight its potential for optimizing personalized therapeutic strategies through language model.

One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure-therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein-protein interaction (PPI) network and biological system profile on the drugs' efficacy-safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.

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.