Triple-drug combination therapy versus six-month proton pump inhibitor monotherapy in non-Helicobacter pylori Helicobacter eradication, and hyperacid environment preference of Helicobacter suis: a clinical study.

BACKGROUND: At present, eradication regimens for non-Helicobacter pylori Helicobacter (NHPH) have not been established yet. We investigated effectiveness of the standard triple-drug combination therapy for Helicobacter pylori eradication and of a proton pump inhibitor (PPI) monotherapy in eradication of NHPH. METHODS: Subjects were the patients who were diagnosed with NHPH-infected gastritis based on microscopic findings, helical-shaped organisms obviously larger than Helicobacter pylori, in the gastric mucosal specimens using Giemsa staining at Kenwakai Hospital between November 2010 and September 2021, whose NHPH species were identified by polymerase chain reaction (PCR) analysis of urease genes in endoscopically-biopsied samples, and who consented to NHPH eradication with either the triple-drug combination therapy for one week or a PPI monotherapy for six months. Six months after the completion of eradication, its result was determined with esophagogastroduodenoscopy, microscopic examination, and PCR analysis. In cases of unsuccessful eradication, a second eradication with the other therapy was suggested to the patient. RESULTS: PCR analysis detected NHPH in 38 patients: 36 as Helicobacter suis and two as Helicobacter heilmannii/Helicobacter ailurogastricus. Fourteen Helicobacter suis-infected and one Helicobacter heilmannii/Helicobacter ailurogastricus-infected patients requested eradication therapy. The triple-drug combination therapy succeeded in four of five patients, while the PPI monotherapy succeeded in five of 10 patients. Three of five patients who had been unsuccessful with the latter therapy requested the triple-drug combination therapy as the second eradication and all three were successful. In total, the triple-drug combination therapy succeeded in seven out of eight (87.5%) attempted cases, while the PPI monotherapy in five out of 10 (50%) attempted cases. CONCLUSIONS: In NHPH eradication, the triple-drug combination therapy was considered to be effective to some extent and to become the first-line therapy. While, although less successful, PPI monotherapy appeared to be a potentially promising option particularly for patients with allergy or resistance to antibiotics. Effectiveness of PPI monotherapy may be attributed to hyperacid environment preference of Helicobacter suis and PPI's acid-suppressive effect. Additionally, male predominance in NHPH-infected gastritis patients may be explained by gender difference in gastric acid secretory capacity. However, further evidence needs to be accumulated. STUDY REGISTRATION: This study was approved by the Research Ethics Committee of Kenwakai Hospital (No. 2,017,024).

Time-dependent ligand-receptor binding kinetics and functionality in a heterodimeric receptor model.

GPCRs heteromerize both in CNS and non-CNS regions. The cell uses receptor heteromerization to modulate receptor functionality and to provide fine tuning of receptor signaling. In order for pharmacologists to explore these mechanisms for therapeutic purposes, quantitative receptor models are needed. We have developed a time-dependent model of the binding kinetics and functionality of a preformed heterodimeric receptor involving two drugs. Two cases were considered: both or only one of the drugs are in excess with respect to the total concentration of the receptor. The latter case can be applied to those situations in which a drug causes unwanted side effects that need to be reduced by decreasing its concentration. The required efficacy can be maintained by the allosteric effects mutually exerted by the two drugs in the two-drug combination system. We discuss this concept assuming that the drug causing unwanted side effects is an opioid and that analgesia is the therapeutic effect. As additional points, allosteric modulation by endogenous compounds and synthetic bivalent ligands was included in the study. Receptor heteromerization offers a mechanistic understanding and quantification of the pharmacological effects elicited by combinations of two drugs at different doses and with different efficacies and cooperativity effects, thus providing a conceptual framework for drug combination therapy.

Anti-ENO1 antibody combined with metformin against tumor resistance: a novel antibody-based platform.

Background: Antibody-based platforms (i.e., ADC) have emerged as one of the most encouraging tools for the cancer resistance caused by cancer stem cells (CSCs) enrichment. Our study might provide a promising therapeutic direction against drug resistance and serve as a potential precursor platform for screening ADC. Methods: The cell migration, invasion, drug resistance, and self-renewal were assessed by the cell invasion and migration assay, wound healing assay, CCK-8 assay, colony formation assay, and sphere formation assay, respectively. The expression profiles of CSCs (ALDH+ and CD44+) subpopulations were screened by flow cytometry. The western blot and cell immunofluorescence assay were used to evaluate pathway-related protein expression in both anti-ENO1 antibody, MET combined with DPP/CTX-treated CSCs. Results: In the present study, western blot and flow cytometry verified that anti-ENO1 antibody target the CD44+ subpopulation by inhibiting the PI3K/AKT pathway, while metformin might target the ALDH+ subpopulation through activation of the AMPK pathway and thus reverse drug resistance to varying degrees. Subsequently, in vitro investigation indicated that anti-ENO1 antibody, metformin combined with cisplatin/cetuximab could simultaneously target ALDH+ and CD44+ subpopulations. The combination also inhibited the CSCs proliferation, migration, invasion, and sphere formation; which may result in overcoming the drug resistance. Then, molecular mechanism exploration verified that the anti-ENO1 antibody, metformin combined with cisplatin/cetuximab inhibited the Wnt/ß-catenin signaling. Conclusions: The study preliminarily revealed anti-ENO1 antibody combined with metformin could overcome drug resistance against CSCs by inhibiting the Wnt//ß-catenin pathway and might serve as a potential precursor platform for screening ADC. More importantly, it is reasonably believed that antibody-based drug combination therapy might function as an encouraging tool for oncotherapy.

A multimodal precision-prevention approach combining lifestyle intervention with metformin repurposing to prevent cognitive impairment and disability: the MET-FINGER randomised controlled trial protocol.

BACKGROUND: Combining multimodal lifestyle interventions and disease-modifying drugs (novel or repurposed) could provide novel precision approaches to prevent cognitive impairment. Metformin is a promising candidate in view of the well-established link between type 2 diabetes (T2D) and Alzheimer's Disease and emerging evidence of its potential neuro-protective effects (e.g. vascular, metabolic, anti-senescence). MET-FINGER aims to test a FINGER 2.0 multimodal intervention, combining an updated FINGER multidomain lifestyle intervention with metformin, where appropriate, in an APOE ε4-enriched population of older adults (60-79 years) at increased risk of dementia. METHODS: MET-FINGER is an international randomised, controlled, parallel-group, phase-IIb proof-of-concept clinical trial, where metformin is included through a trial-within-trial design. 600 participants will be recruited at three sites (UK, Finland, Sweden). Participants at increased risk of dementia based on vascular risk factors and cognitive screening, will be first randomised to the FINGER 2.0 intervention (lifestyle + metformin if eligible; active arm) or to receive regular health advice (control arm). Participants allocated to the FINGER 2.0 intervention group at risk indicators of T2D will be additionally randomised to receive metformin (2000 mg/day or 1000 mg/day) or placebo. The study duration is 2 years. The changes in global cognition (primary outcome, using a Neuropsychological Test Battery), memory, executive function, and processing speed cognitive domains; functional status; lifestyle, vascular, metabolic, and other dementia-related risk factors (secondary outcomes), will be compared between the FINGER 2.0 intervention and the control arm. The feasibility, potential interaction (between-groups differences in healthy lifestyle changes), and disease-modifying effects of the lifestyle-metformin combination will be exploratory outcomes. The lifestyle intervention is adapted from the original FINGER trial (diet, physical activity, cognitive training, monitoring of cardiovascular/metabolic risk factors, social interaction) to be consistently delivered in three countries. Metformin is administered as Glucophage®XR/SR 500, (500 mg oral tablets). The metformin/placebo treatment will be double blinded. CONCLUSION: MET-FINGER is the first trial combining a multimodal lifestyle intervention with a putative repurposed disease-modifying drug for cognitive impairment prevention. Although preliminary, its findings will provide crucial information for innovative precision prevention strategies and form the basis for a larger phase-III trial design and future research in this field. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05109169).

Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation.

Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergistic drug pairs are still lacking. To address this challenge, we systematically analyzed drug combination datasets curated from multiple databases to identify drug pairs more likely to show synergy. We classified drug pairs based on their MoA and discovered that 110 MoA pairs were significantly enriched in synergy in at least one type of cancer. To improve the accuracy of predicting synergistic effects of drug pairs, we developed a suite of machine learning models that achieve better predictive performance. Unlike most previous methods that were rarely validated by wet-lab experiments, our models were validated using two-dimensional cell lines and three-dimensional tumor slice culture (3D-TSC) models, implying their practical utility. Our prediction and validation results indicated that the combination of the RTK inhibitors Lapatinib and Pazopanib exhibited a strong therapeutic effect in breast cancer by blocking the downstream PI3K/AKT/mTOR signaling pathway. Furthermore, we incorporated molecular features to identify potential biomarkers for synergistic drug pairs, and almost all potential biomarkers found connections between drug targets and corresponding molecular features using protein-protein interaction network. Overall, this study provides valuable insights to complement and guide rational efforts to develop drug combination treatments.

Liver fibrosis and MAFLD: the exploration of multi-drug combination therapy strategies.

In recent years, the prevalence of metabolic-associated fatty liver disease (MAFLD) has reached pandemic proportions as a leading cause of liver fibrosis worldwide. However, the stage of liver fibrosis is associated with an increased risk of severe liver-related and cardiovascular events and is the strongest predictor of mortality in MAFLD patients. More and more people believe that MAFLD is a multifactorial disease with multiple pathways are involved in promoting the progression of liver fibrosis. Numerous drug targets and drugs have been explored for various anti-fibrosis pathways. The treatment of single medicines is brutal to obtain satisfactory results, so the strategies of multi-drug combination therapies have attracted increasing attention. In this review, we discuss the mechanism of MAFLD-related liver fibrosis and its regression, summarize the current intervention and treatment methods for this disease, and focus on the analysis of drug combination strategies for MAFLD and its subsequent liver fibrosis in recent years to explore safer and more effective multi-drug combination therapy strategies.

Differences in response to treatment in children with severe IgA nephropathy according to patient age.

AIM: To clarify whether the response to treatment of IgA nephropathy (IgAN) differs depending on patient age, we examined the response to treatment according to age of onset in children with IgAN. METHODS: We collected data for 44 children with severe IgAN. The children were retrospectively divided into three groups based on their age at disease onset. Group 1 consisted of 24 children under 11 years old, group 2 consisted of 9 children aged 12 to 13 years, and group 3 consisted of 11 children aged over 14 years old. The clinical features and prognosis were analyzed for each group. RESULTS: The urinary protein excretion and serum IgA values in group 3 were higher than those in groups 1 and 2 at the most recent follow up, and histological findings showed that the MESTCG scores in group 3 were higher than those in group 1. Furthermore, the incidence of patients with persistent nephropathy or renal insufficiency in group 3 was higher than those in groups 1 and 2. CONCLUSIONS: Patients aged 14 years and older with IgAN may respond poorly to treatment compared with those younger than 14 years old. Therefore, care must be taken regarding response to treatment and relapse when treating older children.

AI-DrugNet: A network-based deep learning model for drug repurposing and combination therapy in neurological disorders.

Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer's disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases.

Influenza Treatment: Limitations of Antiviral Therapy and Advantages of Drug Combination Therapy.

Influenza infection is serious and debilitating for humans and animals. The influenza virus undergoes incessant mutation, segment recombination, and genome reassortment. As a result, new epidemics and pandemics are expected to emerge, making the elimination challenging of the disease. Antiviral therapy has been used for the treatment of influenza since the development of amantadine in the 1960s; however, its use is hampered by the emergence of novel strains and the development of drug resistance. Thus, combinational therapy with two or more antivirals or immunomodulators with different modes of action is the optimal strategy for the effective treatment of influenza infection. In this review, we describe current options for combination therapy, their performance, and constraints imposed by resistance, calling attention to the advantages of combination therapy against severe influenza infections. We also discuss the challenges of influenza therapy and the limitations of approved antiviral drugs.

Discovery, evaluation and mechanism study of WDR5-targeted small molecular inhibitors for neuroblastoma.

Neuroblastoma is the most common and deadliest tumor in infancy. WDR5 (WD Repeat Domain 5), a critical factor supporting an N-myc transcriptional complex via its WBM site and interacting with chromosome via its WIN site, promotes the progression of neuroblastoma, thus making it a potential anti-neuroblastoma drug target. So far, a few WIN site inhibitors have been reported, and the WBM site disruptors are rare to see. In this study we conducted virtual screening to identify candidate hit compounds targeting the WBM site of WDR5. As a result, 60 compounds were selected as candidate WBM site inhibitors. Cell proliferation assay demonstrated 6 structurally distinct WBM site inhibitors, numbering as compounds 4, 7, 11, 13, 19 and 22, which potently suppressed 3 neuroblastoma cell lines (MYCN-amplified IMR32 and LAN5 cell lines, and MYCN-unamplified SK-N-AS cell line). Among them, compound 19 suppressed the proliferation of IMR32 and LAN5 cells with EC50 values of 12.34 and 14.89 µM, respectively, and exerted a moderate inhibition on SK-N-AS cells, without affecting HEK293T cells at 20 µM. Analysis of high-resolution crystal complex structure of compound 19 against WDR5 revealed that it competitively occupied the hydrophobic pocket where V264 was located, which might disrupt the interaction of MYC with WDR5 and further MYC-medicated gene transcription. By performing RNA-seq analysis we demonstrated the differences in molecular action mechanisms of the compound 19 and a WIN site inhibitor OICR-9429. Most interestingly, we established the particularly high synergy rate by combining WBM site inhibitor 19 and the WIN site inhibitor OICR-9429, providing a novel therapeutic avenue for neuroblastoma.

Benefits and risks of drug combination therapy for diabetes mellitus and its complications: a comprehensive review.

Diabetes is a chronic metabolic disease, and its therapeutic goals focus on the effective management of blood glucose and various complications. Drug combination therapy has emerged as a comprehensive treatment approach for diabetes. An increasing number of studies have shown that, compared with monotherapy, combination therapy can bring significant clinical benefits while controlling blood glucose, weight, and blood pressure, as well as mitigating damage from certain complications and delaying their progression in diabetes, including both type 1 diabetes (T1D), type 2 diabetes (T2D) and related complications. This evidence provides strong support for the recommendation of combination therapy for diabetes and highlights the importance of combined treatment. In this review, we first provided a brief overview of the phenotype and pathogenesis of diabetes and discussed several conventional anti-diabetic medications currently used for the treatment of diabetes. We then reviewed several clinical trials and pre-clinical animal experiments on T1D, T2D, and their common complications to evaluate the efficacy and safety of different classes of drug combinations. In general, combination therapy plays a pivotal role in the management of diabetes. Integrating the effectiveness of multiple drugs enables more comprehensive and effective control of blood glucose without increasing the risk of hypoglycemia or other serious adverse events. However, specific treatment regimens should be tailored to individual patients and implemented under the guidance of healthcare professionals.

Reversal of itraconazole resistance in Sporothrix brasiliensis and Sporothrix schenckii by nonsteroidal anti-inflammatory drugs.

BACKGROUND: The emergence of resistant isolates has brought challenges to the treatment of sporotrichosis, prompting the search for new therapeutic strategies. Previous studies reported that nonsteroidal anti-inflammatory drugs (NSAIDs) show in vitro activity against several pathogenic fungi, including species of Candida, Cryptococcus, and Trichosporon. AIMS: This study aimed to investigate the in vitro efficacy of three NSAIDs (acetylsalicylic acid, diclofenac sodium, and ibuprofen), alone and in combination with itraconazole, against eleven clinical isolates of Sporothrix brasiliensis and Sporothrix schenckii. METHODS: Minimal inhibitory concentrations were determined by the broth microdilution method. Drug interactions and the fractional inhibitory concentration index of NSAIDs and itraconazole were assessed by the checkerboard method. RESULTS: When used alone, ibuprofen was the most active NSAID, followed by acetylsalicylic acid. Combinations of NSAIDs with itraconazole showed synergistic antifungal activity against nine isolates. It was also found that itraconazole combined with acetylsalicylic acid, diclofenac sodium, or ibuprofen, led to resistance reversal in two, three, and five of the six drug-resistant isolates, respectively. CONCLUSIONS: The results indicate that the combination of itraconazole and the evaluated NSAIDs are a promising strategy for the treatment of sporotrichosis.

Combination of niclosamide and current therapies to overcome resistance for cancer: New frontiers for an old drug.

Niclosamide is a drug used to treat parasitic infections. Recent studies have shown that niclosamide may have a wide range of clinical applications and can be used to treat cancer and other diseases. However, its application is also limited by its water solubility and safety, and drug resistance to cancer. To solve these problems, some studies have shown that niclosamide can be used in combination with chemotherapeutic drugs, targeted drugs, radiotherapy, and immunotherapy to enhance the anti-tumor effect. This review summarizes the drug combination strategies and therapeutic effect of niclosamide, to provide a reference for the combination therapy of niclosamide and wider application of antitumor drugs.

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations.

Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic functions. Notwithstanding the success of current models, understanding the mechanism of drug synergy from a chemical-gene-tissue interaction perspective lacks study, hindering current algorithms from drug mechanism study. Here, we proposed a deep neural network model termed DTSyn (Dual Transformer encoder model for drug pair Synergy prediction) based on a multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer encoder to capture chemical substructure-gene and gene-gene associations and a coarse-granularity transformer encoder to extract chemical-chemical and chemical-cell line interactions. DTSyn achieved the highest receiver operating characteristic area under the curve of 0.73, 0.78. 0.82 and 0.81 on four different cross-validation tasks, outperforming all competing methods. Further, DTSyn achieved the best True Positive Rate (TPR) over five independent data sets. The ablation study showed that both transformer encoder blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, representing the potential mechanisms of drug action. By leveraging the attention mechanism and pretrained gene embeddings, DTSyn shows improved interpretability ability. Thus, we envision our model as a valuable tool to prioritize synergistic drug pairs with chemical and cell line gene expression profile.

Targeted Co-Delivery of Gefitinib and Rapamycin by Aptamer-Modified Nanoparticles Overcomes EGFR-TKI Resistance in NSCLC via Promoting Autophagy.

Acquired drug resistance decreases the efficacy of gefitinib after approximately 1 year of treatment in non-small-cell lung cancer (NSCLC). Autophagy is a process that could lead to cell death when it is prolonged. Thus, we investigated a drug combination therapy of gefitinib with rapamycin-a cell autophagy activator-in gefitinib-resistant NSCLC cell line H1975 to improve the therapeutic efficacy of gefitinib in advanced NSCLC cells through acute cell autophagy induction. Cell viability and tumor formation assays indicated that rapamycin is strongly synergistic with gefitinib inhibition, both in vitro and in vivo. Mechanistic studies demonstrated that EGFR expression and cell autophagy decreased under gefitinib treatment and were restored after the drug combination therapy, indicating a potential cell autophagy-EGFR positive feedback regulation. To further optimize the delivery efficiency of the combinational agents, we constructed an anti-EGFR aptamer-functionalized nanoparticle (NP-Apt) carrier system. The microscopic observation and cell proliferation assays suggested that NP-Apt achieved remarkably targeted delivery and cytotoxicity in the cancer cells. Taken together, our results suggest that combining rapamycin and gefitinib can be an efficacious therapy to overcome gefitinib resistance in NSCLC, and targeted delivery of the drugs using the aptamer-nanoparticle carrier system further enhances the therapeutic efficacy of gefitinib.

Leucyl-tRNA Synthetase Inhibitor, D-Norvaline, in Combination with Oxacillin, Is Effective against Methicillin-Resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is a pathogenic bacterium that causes severe diseases in humans. For decades, MRSA has acquired substantial resistance against conventional antibiotics through regulatory adaptation, thereby posing a challenge for treating MRSA infection. One of the emerging strategies to combat MRSA is the combinatory use of antibacterial agents. Based on the dramatic change in phospholipid fatty acid (PLFA) composition of MRSA in previous results, this study investigated branched-chain amino acid derivatives (precursors of fatty acid synthesis of cell membrane) and discovered the antimicrobial potency of D-norvaline. The compound, which can act synergistically with oxacillin, is among the three leucine-tRNA synthetase inhibitors with high potency to inhibit MRSA cell growth and biofilm formation. PLFA analysis and membrane properties revealed that D-norvaline decreased the overall amount of PLFA, increasing the fluidity and decreasing the hydrophobicity of the bacterial cell membrane. Additionally, we observed genetic differences to explore the response to D-norvaline. Furthermore, deletion mutants and clinically isolated MRSA strains were treated with D-norvaline. The study revealed that D-norvaline, with low concentrations of oxacillin, was effective in killing several MRSA strains. In summary, our findings provide a new combination of aminoacyl-tRNA synthetase inhibitor D-norvaline and oxacillin, which is effective against MRSA.

Targeting tumour necrosis factor to ameliorate viral pneumonia.

Pneumonia is a serious complication associated with inflammation of the lungs due to infection with viral pathogens. Seasonal and pandemic influenza viruses, variola virus (agent of smallpox) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; agent of COVID-19) are some leading examples. Viral pneumonia is triggered by excessive inflammation associated with dysregulated cytokine production, termed 'cytokine storm'. Several cytokines have been implicated but tumour necrosis factor (TNF) plays a critical role in driving lung inflammation, severe lung pathology and death. Despite this, the exact role TNF plays in the aetiology and pathogenesis of virus infection-induced respiratory complications is not well understood. In this review, we discuss the pathological and immunomodulatory roles of TNF in contributing to immunopathology and resolution of lung inflammation, respectively, in mouse models of influenza- and smallpox (mousepox)-induced pneumonia. We review studies that have investigated dampening of inflammation on the outcome of severe influenza and orthopoxvirus infections. Most studies on the influenza model have evaluated the efficacy of treatment with anti-inflammatory drugs, including anti-TNF agents, in animal models on the day of viral infection. We question the merits of those studies as they are not transferable to the clinic given that individuals generally present at a hospital only after the onset of disease symptoms and not on the day of infection. We propose that research should be directed at determining whether dampening lung inflammation after the onset of disease symptoms will reduce morbidity and mortality. Such a treatment strategy will be more relevant clinically.

Various Uses of PD1/PD-L1 Inhibitor in Oncology: Opportunities and Challenges.

The occurrence and development of cancer are closely related to the immune escape of tumor cells and immune tolerance. Unlike previous surgical, chemotherapy, radiotherapy and targeted therapy, tumor immunotherapy is a therapeutic strategy that uses various means to stimulate and enhance the immune function of the body, and ultimately achieves the goal of controlling tumor cells.With the in-depth understanding of tumor immune escape mechanism and tumor microenvironment, and the in-depth study of tumor immunotherapy, immune checkpoint inhibitors represented by Programmed Death 1/Programmed cell Death-Ligand 1(PD-1/PD-L1) inhibitors are becoming increasingly significant in cancer medication treatment. employ a variety of ways to avoid detection by the immune system, a single strategy is not more effective in overcoming tumor immune evasion and metastasis. Combining different immune agents or other drugs can effectively address situations where immunotherapy is not efficacious, thereby increasing the chances of success and alternative access to alternative immunotherapy. Immune combination therapies for cancer have become a hot topic in cancer treatment today. In this paper, several combination therapeutic modalities of PD1/PD-L1 inhibitors are systematically reviewed. Finally, an analysis and outlook are provided in the context of the recent advances in combination therapy with PD1/PD-L1 inhibitors and the pressing issues in this field.

Machine learning approaches for drug combination therapies.

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.