Drug Combinations Research Articles

Celastrol Derivative/DOX Co-Assembled Nanodrug for Enhanced Antitumor Therapy.

Multidrug resistance (MDR) is a key challenge in clinical chemotherapy. The combination of drugs can effectively reverse multi-drug resistance. In this study, doxorubicin (DOX) was capsulated into nanoparticles formed by an amphiphilic PEGylated-poly (α-lipoic acid)-methanamide analogue of celastrol (mPEG-PαLA-CEN) prodrug polymer. CEN was linked to the branched chain of poly (α-lipoic acid) by forming ester bonds. DOX was physically trapped inside the nanoparticles via electrostatic interaction. Both drugs can be simultaneously released in response to low pH and high GSH in order to overcome DOX resistance. The chemical structure of the mPEG-PαLA-CEN-DOX NPs was confirmed through 1H NMR, FT-IR spectroscopy, UV-Vis spectrum, DLS, and TEM. Drug-loading content, efficacy, and drug release were measured using HPLC. Cell toxicity was examined using an MTT assay. CEN/DOX-loaded nanoparticles were found to have spherical shapes with diameters of around 229.7 nm. The NPs exhibited high biocompatibility and released 92% DOX and 71.8% CEN in response to low pH and high GSH of tumor microenvironments. As dual drug-loaded nanoparticles, the efficacy of mPEG-PαLA-CEN-DOX NPs against tumor cell lines in vitro was enhanced for both MCF-7 and MCF-7/ADR compared to free DOX. Compared to free DOX, the IC50 of mPEG-PαLA-CEN-DOX NPs reduced from 46.10 μM to 8.36 μM for the MCF-7/ADR cell line. In conclusion, this study demonstrated that PEGylated poly (α-lipoic acid)-CEN copolymers can be used not only as biocompatible, stimulation-responsive anticancer drug nanocarriers but also as chemosensitizers to overcome multidrug resistance, which provide a theoretical base for clinical application of CEN/DOX nanodrug.

DDSBC: A Stacking Ensemble Classifier-Based Approach for Breast Cancer Drug-Pair Cell Synergy Prediction.

Breast cancer (BC) ranks as a leading cause of mortality among women worldwide, with incidence rates continuing to rise. The quest for effective treatments has led to the adoption of drug combination therapy, aiming to enhance drug efficacy. However, identifying synergistic drug combinations remains a daunting challenge due to the myriad of potential drug pairs. Current research leverages machine learning (ML) and deep learning (DL) models for drug-pair synergy prediction and classification. Nevertheless, these models often underperform on specific cancer types, including BC, as they are trained on data spanning various cancers without any specialization. Here, we introduce a stacking ensemble classifier, the drug-drug synergy for breast cancer (DDSBC), tailored explicitly for BC drug-pair cell synergy classification. Unlike existing models that generalize across cancer types, DDSBC is exclusively developed for BC, offering a more focused approach. Our comparative analysis against classical ML methods as well as DL models developed for drug synergy prediction highlights DDSBC's superior performance across test and independent datasets on BC data. Despite certain metrics where other methods narrowly surpass DDSBC by 1-2%, DDSBC consistently emerges as the top-ranked model, showcasing significant differences in scoring metrics and robust performance in ablation studies. DDSBC's performance and practicality position it as a preferred choice or an adjunctive validation tool for identifying synergistic or antagonistic drug pairs in BC, providing valuable insights for treatment strategies.

Proven anti-virulence therapies in combating methicillin- and vancomycin-resistant Staphylococcus aureus infections.

Despite years of efforts to develop new antibiotics for eradicating multidrug-resistant (MDR) and multi-virulent Methicillin-Resistant Staphylococcus aureus (MRSA) and Vancomycin-Resistant Staphylococcus aureus (VRSA) infections, treatment failures and poor prognoses in most cases have been common. Therefore, there is an urgent need for new therapeutic approaches targeting virulence arrays. Our aim is to discover new anti-virulence therapies targeting MRSA and VRSA virulence arrays. We employed phenotypic, molecular docking, and genetic studies to screen for anti-virulence activities among selected promising compounds: Coumarin, Simvastatin, and Ibuprofen. We found that nearly all detected MRSA and VRSA strains exhibited MDR and multi-virulent profiles. The molecular docking results aligned with the phenotypic and genetic assessments of virulence production. Biofilm and hemolysin productions were inhibited, and all virulence genes were downregulated upon treatment with sub-minimum inhibitory concentration (sub-MIC) of these promising compounds. Ibuprofen was the most active compound, exhibiting the highest inhibition and downregulation of virulence gene products. Moreover, in vivo and histopathological studies confirmed these results. Interestingly, we observed a significant decrease in wound area and improvements in re-epithelialization and tissue organization in the Ibuprofen and antimicrobial treated group compared with the group treated with antimicrobial alone. These findings support the idea that a combination of Ibuprofen and antimicrobial drugs may offer a promising new therapy for MRSA and VRSA infections. We hope that our findings can be implemented in clinical practice to assist physicians in making the most suitable treatment decisions.

Memantine and the Kynurenine Pathway in the Brain: Selective Targeting of Kynurenic Acid in the Rat Cerebral Cortex.

Cytoprotective and neurotoxic kynurenines formed along the kynurenine pathway (KP) were identified as possible therapeutic targets in various neuropsychiatric conditions. Memantine, an adamantane derivative modulating dopamine-, noradrenaline-, serotonin-, and glutamate-mediated neurotransmission is currently considered for therapy in dementia, psychiatric disorders, migraines, or ischemia. Previous studies have revealed that memantine potently stimulates the synthesis of neuroprotective kynurenic acid (KYNA) in vitro via a protein kinase A-dependent mechanism. Here, the effects of acute and prolonged administration of memantine on brain kynurenines and the functional changes in the cerebral KP were assessed in rats using chromatographic and enzymatic methods. Five-day but not single treatment with memantine selectively activated the cortical KP towards neuroprotective KYNA. KYNA increases were accompanied by a moderate decrease in cortical tryptophan (TRP) and L-kynurenine (L-KYN) concentrations without changes in 3-hydroxykynurenine (3-HK) levels. Enzymatic studies revealed that the activity of cortical KYNA biosynthetic enzymes ex vivo was stimulated after prolonged administration of memantine. As memantine does not directly stimulate the activity of KATs' proteins, the higher activity of KATs most probably results from the increased expression of the respective genes. Noteworthy, the concentrations of KYNA, 3-HK, TRP, and L-KYN in the striatum, hippocampus, and cerebellum were not affected. Selective cortical increase in KYNA seems to represent one of the mechanisms underlying the clinical efficacy of memantine. It is tempting to hypothesize that a combination of memantine and drugs could strongly boost cortical KYNA and provide a more effective option for treating cortical pathologies at early stages. Further studies should evaluate this issue in experimental animal models and under clinical scenarios.

Synergistic Effects of Artesunate in Combination with Amphotericin B and Miltefosine against Leishmania infantum: Potential for Dose Reduction and Enhanced Therapeutic Strategies.

Leishmaniasis is a tropical infectious disease caused by Leishmania parasites. The disease can be spread by the bite of an infected sand fly. Currently, five chemotherapeutic drugs are available in leishmaniasis treatment. However, these drugs exhibit toxicity and serious adverse effects on infected individuals, necessitating alternative treatment strategies. One such strategy involves using combinations of existing antileishmanial drugs. In this study, we evaluated the interaction between artesunate (AS) and three antileishmanial drugs-amphotericin B (AmB), miltefosine (MF), and paromomycin (PM) against Leishmania infantum. This evaluation marks the first time such an assessment has been conducted. The Chou-Talalay combination index method was employed to analyze the drug interaction. The findings revealed that the interaction between AS and AmB ranged from antagonistic to synergistic, while the interaction between AS and MF showed moderate to strong synergism. In contrast, the interaction between AS and PM resulted in an antagonistic interaction, which differs from the combinations with AmB or MF. This study provides valuable insights for developing novel drug regimens for leishmaniasis treatment, emphasizing the potential of AS and its combination with existing antileishmanial drugs. Further research is necessary to optimize drug combinations and minimize adverse effects, leading to more effective therapeutic outcomes.

Murepavadin Enhances the Killing Efficacy of Ciprofloxacin against Pseudomonas aeruginosa by Inhibiting Drug Efflux.

Pseudomonas aeruginosa is a multidrug-resistant Gram-negative pathogen and one of the leading causes of ventilator-associated pneumonia and infections in patients with chronic obstructive pulmonary disease and cystic fibrosis. Murepavadin is a peptidomimetic that specifically targets outer-membrane lipopolysaccharide transport protein LptD of P. aeruginosa. In this study, we find that murepavadin enhances the bactericidal efficacy of ciprofloxacin. We further demonstrate that murepavadin increases intracellular accumulation of ciprofloxacin by suppressing drug efflux. In addition, the murepavadin-ciprofloxacin combination exhibits a synergistic bactericidal effect in an acute murine pneumonia model. In conclusion, our results identify an effective drug combination for the treatment of P. aeruginosa infections.

Effects and Mechanisms of the Xianhecao-Huanglian Drug Pair on Autophagy-Mediated Intervention in Acute Inflammatory Bowel Disease via the JAK2/STAT3 Pathway

To explore the effects and mechanisms of the Xianhecao-Huanglian drug pair on autophagy-mediated intervention in acute inflammatory bowel disease (IBD) via the JAK2/STAT3 pathway. The study examined the underlying mechanisms of action of Xianhecao (APL) and Huanglian (CR) using a mouse model of dextran sodium sulfate (DSS)-induced acute inflammatory bowel disease (IBD) and in an in vitro model of IBD induced by lipopolysaccharide (LPS). The assessment of the therapeutic efficacy of the Xianhecao-Huanglian drug combination in a mouse model of IBD caused by DSS included the following parameters: Assessment of weight loss or gain. Measurement of the disease activity index (DAI). Assessment of histological damage. Determination of organ index. Measurement of colon length. Ascertain the levels of inflammatory cytokines in the intestinal tissues and serum of mice. Immunohistochemistry (IHC) for the measurement of tight junction protein concentrations in the colon mucosa, including ZO-1, claudin-1, and occludin. Measurement of mucin levels, specifically Mucin 2 (Muc2). Hematoxylin and eosin (HE) staining for the observation of histopathological alterations in colonic tissues. Examining the effect on goblet cells using periodic acid-Schiff (PAS) labeling. Application of Western blot and immunofluorescence techniques for the detection of autophagy-related markers in colonic tissues and proteins associated with the JAK2/STAT3 pathway. A cell inflammation model of IBD was induced through LPS stimulation, and a serum containing the Xianhecao-Huanglian drug pair (referred to as ACHP-DS) was formulated. Cell viability, anti-proinflammatory cytokines, tight junction proteins, mucins, autophagy-related markers, and the JAK2/STAT3 signaling pathway were assessed. The Xianhecao-Huanglian drug pair significantly ameliorated the symptoms and survival quality of acute IBD mice, reducing the disease activity index score, raising MUC2 secretion and tight junction protein expression to improve the integrity of the intestinal barrier, and preserving goblet cell function; thus, protecting the intestines. It effectively restrained triggering the signaling pathway that involves JAK2 and STAT3, leading to the suppression of inflammation and amelioration of colonic inflammation damage. Additionally, it induced autophagy in mouse colonic tissues.The in vitro experiments demonstrated that the Xianhecao-Huanglian drug combination enhanced the viability of LOVO and NCM460 cells when exposed to LPS stimulation. Furthermore, it suppressed the production of inflammatory cytokines such as IL-6, IL-1β, as well as TNF-α, whilst increasing the production of IL-10, ZO-1, along with MUC2. These effects collectively led to the alleviation of inflammation and the restoration of mucosal integrity. The results were consistent with what was shown in the in vivo trial. Moreover, the medication demonstrated effectiveness in reducing JAK2 along with STAT3 phosphorylation levels in the LPS-induced inflammatory model of IBD cells. The intervention with either the Xianhecao-Huanglian drug combination-containing serum or the JAK2/STAT3 pathway inhibitor AG490 reversed the pro-inflammatory effects and increased autophagy levels in the LPS-stimulated cells. The Xianhecao-Huanglian drug combination modulates the JAK2/STAT3 pathway, leading to the induction of autophagy, which serves as an intervention for IBD.

Phosphodiesterase-5 inhibitors and hearing impairment: a disproportionality analysis using the US food and drug administration adverse event reporting system

ABSTRACT Background This study aimed to investigate the association between phosphodiesterase-5 inhibitors (PDE-5i) and hearing impairment adverse events (HIAEs) while providing an overview of the characteristics of drug-related HIAEs. Research design and methods We conducted a detailed pharmacovigilance analysis using data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database, covering 2004 to 2022. By calculating the reporting odds ratio (ROR) and the information component (IC), we identified signals that indicate the association between PDE-5i use and HIAEs. Results Among the 191,398 reports related to PDE-5i, we identified 2,608 cases of HIAEs. Signals were observed for both PDE-5i monotherapy and polytherapy, indicating combinations of drugs. Avanafil exhibited the strongest signal (ROR: 4.35, 95% CI: 2.56–7.41, IC: 2.09, 95% CI: 0.10–3.51), while vardenafil showed the weakest signal (ROR: 2.69, 95% CI: 2.21–3.28, IC: 1.14, 95% CI: 0.74–2.04). Sildenafil had the highest reported cases (ROR: 3.03, 95% CI: 2.82–3.24, IC: 1.57, 95% CI: 1.34–1.80). Conclusion These findings highlight a significant correlation between PDE-5i use and HIAEs, emphasizing the need for careful evaluation in clinical practice and providing appropriate guidance to patients before initiating treatment.

Reusable vs Single Use: what Are the Device Trade-Offs in Improving Sustainability.

As autoinjector requirements become increasingly diverse and pharma companies look for quicker routes to market, with lower costs and improved sustainability, there is an increasing trend towards devices with a reusable element. The flexibility in reusable elements can be beneficial for pharma companies with access to these platforms, allowing a relatively rapid transition between different drug combinations. However, it can also lead to devices designed to cover a wide range of requirements which are over designed for their actual more limited end use. The challenge of creating both a cost and sustainability optimised platform device is significantly harder than if developing a single use device with a specific purpose in mind. This paper looks at the range of reusable products on the market, examining some of the assertions around the cost and sustainability benefits of these devices as well as where there are trade-offs relative to current single use format devices.

Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment

By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on normalized advantage function reinforcement learning. It aims to boost the body's immune response against cancer cell proliferation. This control approach is applied to provide a combination of both chemotherapy and anti-angiogenic drugs for the first time without the need for complex, predefined mathematical models. It employs a model-free reinforcement learning technique that adaptively adjusts to individual patients to determine optimal drug administration with minimum injection rates. In this regard, a comprehensive and realistic simulation and training environment is employed, with the concentrations of normal cells, cancer cells, and endothelial cells, as well as the levels of chemotherapy and anti-angiogenic agents, as state variables. Furthermore, high levels of disturbances are considered in the simulation to investigate the robustness of the proposed method against probable uncertainties in the treatment process or patient parameters. A practical reward function has also been devised in alignment with medical objectives to ensure effective and safe treatment outcomes. The results demonstrate robustness and superior performance compared to the existing methods. Simulations show that the proposed approach is a dependable strategy for effectively reducing the concentration of cancer cells in the shortest duration using minimal doses of chemotherapy and anti-angiogenic drugs.

MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma.

This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose-response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations.

Exploring the mechanisms of Huangqin Qingfei Decoction on acute lung injury by LC-MS combined network pharmacology analysis

BackgroundAcute lung injury (ALI) is a respiratory disease characterized by pulmonary inflammation and increased microvascular permeability, resulting in significant mortality and a lack of effective pharmacological treatment. Huangqin Qingfei Decoction (HQQFD), a Traditional Chinese Medicine (TCM) prescription known for its heat-clearing and detoxifying properties, has shown efficacy in treating ALI. However, the underlying mechanisms of HQQFD to against ALI remain to be elucidated. PurposeThis study aims to discover the mechanisms and the principal bioactive compounds contributing to HQQFD's protective effects in the treatment of ALI. MethodsAn ultra-high performance liquid chromatography-Orbitrap high-resolution mass spectrometry (UHPLC-Orbitrap HRMS) method was employed to characterize the chemical profile in HQQFD and xenobiotics (prototypes and metabolites) in rat lung tissue. Based on prototypes identified, a symptom-guided pharmacological networks of ALI were performed. Molecular docking and extensive literature reviews were conducted to validate our findings. ResultsA total of 105 compounds were identified in HQQFD, and a total of 194 HQQFD-related xenobiotics (30 prototypes and 163 metabolites) were detected in rat lung tissue. Based on prototypes identified in rat lung, a symptom-guided pharmacological networks of ALI were constructed, AKT1, TNF, EGFR, MMP2, GSK3B, STAT3, MAPK8, IL-6, CDK2 and TP53 were finally identified as key targets. Subsequently, 11 compounds with protective and therapeutic activity were selected by molecular docking analysis, including genipin 1-gentiobioside, chrysin-6-C-α-L-arabinoside-8-C-β-d-glucoside, scutellarin, chrysin-6-C-β-d-glucoside-8-C-α-L-arabinoside, 6’’-O-[(E)-p-coumaroyl] genipin-gentiobioside, apigenin 7-O-glucoside, baicalin, dihydrobaicalin, wogonoside, crocin I, crocetin. Bioinformatics and literature analysis suggested that, baicalin, wogonoside, genipin 1-gentiobioside and crocetin may be the primary active compounds of HQQFD, potentially targeting GSK3B, MAPK8, IL-6, AKT1 and TNF for HQQFD in addressing ALI. The therapeutic effects of HQQFD may be mediated through the IL-17 and PI3K-AKT signaling pathways. ConclusionThe predominant components of HQQFD against ALI are baicalein, wogonoside, genipin 1-gentiobiosid and crocetin, with the IL-17 and PI3K-AKT pathways playing crucial roles. This study provides a foundational guide for future research and introduces innovative methods for exploring the mechanisms of other drug combinations or TCM formulas.

Time-of-day effects of cancer drugs revealed by high-throughput deep phenotyping

The circadian clock, a fundamental biological regulator, governs essential cellular processes in health and disease. Circadian-based therapeutic strategies are increasingly gaining recognition as promising avenues. Aligning drug administration with the circadian rhythm can enhance treatment efficacy and minimize side effects. Yet, uncovering the optimal treatment timings remains challenging, limiting their widespread adoption. In this work, we introduce a high-throughput approach integrating live-imaging and data analysis techniques to deep-phenotype cancer cell models, evaluating their circadian rhythms, growth, and drug responses. We devise a streamlined process for profiling drug sensitivities across different times of the day, identifying optimal treatment windows and responsive cell types and drug combinations. Finally, we implement multiple computational tools to uncover cellular and genetic factors shaping time-of-day drug sensitivity. Our versatile approach is adaptable to various biological models, facilitating its broad application and relevance. Ultimately, this research leverages circadian rhythms to optimize anti-cancer drug treatments, promising improved outcomes and transformative treatment strategies.

Mycobacterium marseillense bloodstream infection combined with skin fungal infection: a case report and literature review

BackgroundNon-tuberculous mycobacteria (NTM) are present widely in the natural environment and can invade the human body through the respiratory tract, gastrointestinal tract, and skin. Immunocompromised patients are particularly prone to infection, which primarily affects multiple organs, including the lungs, lymph nodes, and skin. However, cases of NTM bloodstream infections are rare. Here, we report a rare case of Mycobacterium marseillense bloodstream infection with concurrent skin fungal infection in a patient after kidney transplantation. Related literature was reviewed to enhance the understanding of this rare condition.Case presentationA 58-year-old male with a history of long-term steroid and immunosuppressant use after kidney transplantation presented with limb swelling that worsened over the past two months. Physical examination revealed redness and swelling of the skin in all four limbs, with a non-healing wound on the lower left limb. Skin tissue analysis by metagenomic next-generation sequencing (mNGS) and fungal culture indicated infection with Trichophyton rubrum. Blood culture results suggested infection with Mycobacterium marseillense. After receiving anti-NTM treatment, the patient’s symptoms significantly improved, and he is currently undergoing treatment.ConclusionMycobacterium marseillense is a NTM. Gram staining suffered from misdetection, and the acid-fast staining result was positive. This bacterium was identified by mass spectrometry and mNGS analyses. Antimicrobial susceptibility tests for NTM were performed using the broth microdilution method. The results of the susceptibility test showed that Mycobacterium marseillense was sensitive to clarithromycin, an intermediary between moxifloxacin and linezolid. Bacterial clearance requires a combination of drugs and an adequate course of treatment. NTM bloodstream infections are relatively rare, and early identification and proactive intervention are key to their successful management.

Co-delivery of protopanaxatriol/icariin into niche cells restores bone marrow niches to rejuvenate HSCs for chemotherapy-induced myelosuppression

BackgroundUp to 80 % of chemotherapeutic drugs induce myelosuppression in patients. Chemotherapy not only impairs of hematopoietic stem cells (HSCs) but also damages bone marrow niches (vascular and endosteal). Current treatments for myelosuppression overlook these chemotherapy-induced damages to bone marrow niches and the critical role of niche restoration on hematopoietic regeneration. Ginsenoside protopanaxatriol (PPT) protects vascular endothelium from injury, while icariin (ICA) promotes osteogenic differentiation. The combination of PPT and ICA aims to restore damaged vascular and endosteal niches, thus rejuvenating HSCs for treating myelosuppression. PurposeThis study aims to develop effective, bone marrow niche-directed PPT/ICA therapies for treating chemotherapy-induced myelosuppression. Methods3D cell spheroids were used to investigate the effects of PPT/ICA on cell-cell interactions in vascular niches, osteogenesis, and extracellular matrix (ECM) secretion in endosteal niches. In vitro mimic niche models were designed to access the drug combination's efficacy in rejuvenating and mobilizing in HSCs within bone marrow niches. The delivery capability of PPT/ICA to key niche cell types (mesenchymal stromal cells (MSCs), endothelial cells (ECs), and osteoblasts (OBs)) via nanocarriers has been determined. DSS6 peptide-modified nanoparticles (DSS6-NPs) were prepared for specific co-delivery of PPT/ICA into key niche cell populations in vivo. ResultsPPT can prevent vascular niche injury by restoring vascular EC cell-cell adhesion and the intercellular interactions between ECs and MSCs in 5-fluorouracil (5-FU)-damaged cell spheroids. ICA repaired 5-FU-damaged endosteal niches by promoting osteogenesis and ECM secretion. The combination of PPT and ICA restores key HSC niche factor gene expressions, normalizing HSC differentiation and mobilization. The in vitro cellular uptake efficiency of nanocarriers in a mimic niche is positively correlated with their in vivo delivery into bone marrow niche cells. DSS6-NPs greatly enhance the delivery of PPT/ICA into MSCs and OBs within bone marrow niches. Co-loading of PPT/ICA into DSS6-NPs effectively repairs damaged bone marrow niches and promotes HSC rejuvenation in vivo. ConclusionThe combination of PPT and ICA effectively prevents injury to the vascular and endosteal niches, thereby promoting hematopoietic regeneration in the bone marrow. This study provides novel niche-directed PPT/ICA therapies for managing chemotherapy-induced myelosuppression.

Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O’Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow

Amphotericin B and micafungin duo-loaded nanoemulsion as a potential strategy against Candida auris biofilms

Candida auris is a multidrug-resistant yeast that has seen a worrying increase during the COVID-19 pandemic. Give7/n this, new therapeutic options, such as controlled-release nanomaterials, may be promising in combating the infection. Therefore, this study aimed to develop amphotericin B (AmB) and micafungin (MICA)-loaded nanoemulsions (NEMA) and evaluated against biofilms of C. auris. Nanoemulsions (NEs) were characterized and determined minimum inhibitory concentration MIC90, checkerboard and anti-biofilm. NEMA presented a size of 53.7 and 81.4 nm for DLS and NTA, respectively, with good stability and spherical morphology. MICAmB incorporated efficiency was 88.4 and 99.3%, respectively. The release results show that AmB and MICA obtained a release of 100 and 63.4%, respectively. MICAmB and NEMA showed MIC90 values of 0.015 and 0.031 ug/mL, respectively and synergism. NEMA showed greater metabolic inhibition and morphological changes in mature biofilms. This drugs combination and co-encapsulation proved to be a promising therapy against C. auris biofilms.

Haloperidol for Pain Management: A Narrative Review.

The use of haloperidol in pain management has been a topic of interest for several decades. Haloperidol is a widely used antipsychotic medication with unique pharmacologic properties that make it a potential candidate for pain management. However, the efficacy and safety of haloperidol for pain management remain controversial. This narrative review provides a summary of the current literature on the use of haloperidol for pain management, including its pharmacology, clinical effectiveness, adverse effects, and dosing regimens. We performed a comprehensive search of the literature for this review. The most robust clinical data from the past decade suggest that haloperidol has good efficacy in the treatment of pain related to gastroparesis and migraines and has shown promise for opioid use reduction in patients with chronic pain or receiving palliative care. The overall side effect profile is excellent, with zero reported events of QT-related cardiac arrest and minimal reports of sedation and transient extrapyramidal effects such as akathisia. Dosing regimens used were heterogeneous, with most ranging from 1 to 5 mg per dose via intravenous, intramuscular, or oral route. Studies with designs that isolated the effects of haloperidol from combinations of other drugs were extremely limited. Further high-quality prospective studies are needed to determine the ideal role of haloperidol in the routine clinical management of painful conditions.

Research Progress on the Combination of Acupuncture and Drugs for Treating Sequelae of Stroke

Research Progress on the Combination of Acupuncture and Drugs for Treating Sequelae of Stroke