Liver fibrosis is a pathological process that endangers human health, for which effective treatments remain elusive to date. Paeoniflorin (PAE), a pineane-type monoter penoid compound from the traditional Chinese medicine PaeoniaeRubra Radix, and metformin (MET), an oral biguanide hypoglycemic agent, both demonstrate anti-inflammatory and hepatoprotective effects. In current work, we first discovered that the combined treatment of PAE and MET synergistically inhibited the progression of liver fibrosis in two different animal models: therapeutic and preventive. This therapeutic effect is evidenced by a reduction in the expression levels of liver fibrosis markers and an improvement in histopathological characteristics. Mechanistic exploration further revealed that this combination therapy downregulated the expression of TGF-β1 and p-Smad2, while upregulating Smad7 expression in both models. Importantly, we also found that this combinatorial approach significantly reduced hepatotoxicity and nephrotoxicity in both models. Our findings suggest an effective combination therapy for liver fibrosis and provide the possibility of therapeutic improvement for patients with liver fibrosis.

Antibiotic resistance is a growing global problem that requires innovative therapeutic approaches and strategies for administering antibiotics. One promising approach is combination therapy, in which two or more drugs are combined to combat an infection. Along this line, the combination of antimicrobial peptides (AMPs) with conventional antibiotics has gained attention mainly due to the complementary mechanisms of action of AMPs and conventional antibiotics. In this article, we review both in vitro and in vivo studies that explore the synergy between AMPs and antibiotics. We highlight several mechanisms through which synergy is observed in in vitro experiments, including increasing membrane permeability, disrupting biofilms, directly potentiating antibiotic efficacy, and inhibiting resistance development. Moreover, in vivo studies reveal additional mechanisms such as enhanced/modulated immune responses, reduced inflammation, and improved tissue regeneration. Together, the current literature demonstrates that AMP-antibiotic combinations can substantially enhance efficacy of antibiotic therapies, including therapies against resistant bacteria, which represents a valuable enhancement to current antimicrobial strategies.

BACKGROUND: Olaparib is a PARP inhibitor inducing synthetic lethality in tumors with deficient homologous recombination (HRD) caused by BRCA1/2 mutations. The FDA has approved monotherapy for first-line platinum-sensitive, recurrent high-grade epithelial ovarian cancer. Combination therapy alongside DNA-damaging therapeutics is a promising solution to overcome the limited efficacy in patients with HRD. The present study was designed to develop topotecan- and olaparib-loaded liposomes (TLL and OLL) and assess the effectiveness of their combination in patient-derived ovarian cancer samples.
METHODS: We used HEOC, four clear-cell tumors (EOC 1-4), malignant ascites, and an OCI-E1p endometrioid primary ovarian cancer cell line and performed NGS analysis of BRCA1/2 mutation status. Antiproliferative activity was determined with the MTT assay. The Chou-Talalay algorithm was used to investigate the in vitro pharmacodynamic interactions of TLLs and OLLs.
RESULTS: The OLL showed significantly higher efficacy in all ovarian cancer types with wild-type BRCA1/2 than a conventional formulation, suggesting potential for increased in vivo efficacy. The TLL revealed substantially higher toxicity to EOC 1, EOC 3, ascites and lower toxicity to HEOC than the standard formulation, suggesting better therapeutic efficacy and safety profile. The combination of studied compounds showed a higher reduction in cell viability than drugs used individually, demonstrating a synergistic antitumor effect at most of the selected concentrations.
CONCLUSIONS: The concentration-dependent response of different ovarian cancer cell types to combination therapy confirms the need for in vitro optimization to maximize drug cytotoxicity. The OLL and TLL combination is a promising formulation for further animal studies, especially for eliminating epithelial ovarian cancer with wild-type BRCA1/2.

UNASSIGNED: Through an analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS), we explored the signal strength of adverse reactions (ADRs) related to myopathy caused by the combination of colchicine and statins and gained insight into the characteristics of these myopathy related ADRs.
UNASSIGNED: We extracted data from the FAERS database about ADRs in individuals with myopathy resulting from the combination of colchicine and statins. The analysis was conducted for the period spanning from January 2004 to December 2023 using the reported odds ratio (ROR) and information component (IC) methods to assess muscle-related ADR signals.
UNASSIGNED: A total of 18,386 reports of statin myopathy-associated adverse reactions, 348 colchicine myopathy-associated adverse reactions, and 461 muscle-associated adverse reactions due to the combination of the two were collected; the strongest signals of statin myotoxicity events were for necrotizing myositis (ROR 50.47, 95% CL 41.74-61.01; IC 3.70 95% CL 3.25-4.08); the strongest signal for colchicine myotoxicity events was toxic myopathy (ROR 32.50, 95% CL 19.74-53.51; IC 4.97 95% CL 1.89-5.10), and the strongest signal for statins combined with colchicine was toxic myopathy (ROR 159.85, 95% CL 111.60-228.98; IC 7.22 95% CL 3.59-5.9); muscle-related adverse reactions signals were meaningful when the two drugs were combined in the order of colchicine combined with fluvastatin (ROR 187.38, 95% CL 96.68-363.17; IC 6.99 95% CL 1.65-5.68); colchicine combined with simvastatin in 135 cases (ROR 30.08. 95% CL 25.25-35.85; IC 4.80 95% CL 3.96-5.12); and colchicine combined with rosuvastatin (ROR 25.73, 95% CL 20.16-32.83; IC 4.59 95% CL 3.38-4.98) versus colchicine combined with atorvastatin (ROR 25.73, 95% CL 22.33-29.66; IC 4.59 95% CL 3.97-4.91) with almost identical signal intensity, followed by colchicine combined with pravastatin (ROR 13.67, 95% CL 9.17-20.37; IC 3.73 95% CL 1.87-4.47), whereas no signals were generated for lovastatin or pitavastatin.
UNASSIGNED: Similar ADRs can occur when colchicine and statins are used individually or in combination; however, the strength of these reactions may differ. To minimize the risk of drug interactions, statins with less potential interactions, such as lovastatin, pitavastatin, and pravastatin, should be chosen, and myopathy-related indices and symptoms should be closely monitored during use.

This study investigated the impact of Valencia KK4-type peanut skin ethanolic extract (KK4-PSE) combined with cisplatin or 5-fluorouracil (5-FU) on HeLa cells in vitro and in xenograft models. At exposure times of 24, 48 and 72 h, KK4-PSE inhibited the growth of HeLa cells with a half maximal inhibitory concentration (IC50) of 79.43 ± 0.54, 55.55 ± 1.57 and 41.32 ± 0.74 µg/mL, respectively. Drug interactions evaluated by the Chou-Talalay method demonstrated that KK4-PSE enhanced antiproliferative activity of 5-FU against HeLa cells with combination index (CI) values of 0.49 (48 h) and 0.60 (72 h), indicating a synergistic effect, while KK4-PSE combined with cisplatin exhibited an additive effect (CI = 1.02) at 72 h, and an antagonistic effect at 24 and 48 h exposures (CI = 1.12 and 1.18, respectively). In nude mouse xenograft models, the combination of 5-FU and KK4-PSE markedly reduced HeLa tumor weights compared with the control and single agent treatments groups. The combination of KK4-PSE and 5-FU achieved greater tumor growth inhibition than that of the KK4-PSE-cisplatin combination. KK4-PSE mitigated hepatotoxicity induced by both cisplatin and 5-FU in nude mice. The spleen hyaloserositis was significantly reduced in the combination treatment of 5-FU and KK4-PSE. These results suggest that KK4-PSE has the potential to limit cervical cancer cell proliferation while reducing the toxicity of cisplatin and 5-FU.

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs-those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence-Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a \'fair\' comparison. Yet, exceptions exist for drugs with steep dose-response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio-even if not always optimal-is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.

Despite significant progress in cancer prevention, screening, and treatment, the still limited number of therapeutic options is an obstacle towards increasing the cancer cure rate. In recent years, many efforts were put forth to develop therapeutics that selectively target different components of the oncogenic Wnt/β-catenin signaling pathway. These include small molecule inhibitors, antibodies, and more recently, gene-based approaches. Although some of them showed promising outcomes in clinical trials, the Wnt/β-catenin pathway is still not targeted in routine clinical practice for cancer management. As for most anticancer treatments, a critical limitation to the use of Wnt/β-catenin inhibitors is their therapeutic index, i.e., the difficulty of combining effective anticancer activity with acceptable toxicity. Protecting healthy tissues from the effects of Wnt/β-catenin inhibitors is a major issue due to the vital role of the Wnt/β-catenin signaling pathway in adult tissue homeostasis and regeneration. In this review, we provide an up-to-date summary of clinical trials on Wnt/β-catenin pathway inhibitors, examine their anti-tumor activity and associated adverse events, and explore strategies under development to improve the benefit/risk profile of this therapeutic approach.

The clinical management of malignant tumours is challenging, often leading to severe adverse effects and death. Drug resistance (DR) antagonises the effectiveness of treatments, and increasing drug dosage can worsen the therapeutic index (TI). Current efforts to overcome DR predominantly involve the use of drug combinations, including applying multiple anti-cancerous drugs, employing drug sensitisers, which are chemical agents that enhance pharmacokinetics (PK), including the targeting of cellular pathways and regulating pertinent membrane transporters. While combining multiple compounds may lead to drug-drug interactions (DDI) or polypharmacy effect, the use of drug sensitisers permits rapid attainment of effective treatment dosages at the disease site to prevent early DR and minimise side effects and will reduce the chance of DDI as lower drug doses are required. This review highlights the essential use of TI in evaluating drug dosage for cancer treatment and discusses the lack of a unified standard for TI within the field. Commonly used benefit-risk assessment criteria are summarised, and the critical exploration of the current use of TI in the pharmaceutical industrial sector is included. Specifically, this review leads to the discussion of drug sensitisers to facilitate improved ratios of effective dose to toxic dose directly in humans. The combination of drug and sensitiser molecules might see additional benefits to rekindle those drugs that failed late-stage clinical trials by the removal of detrimental off-target activities through the use of lower drug doses. Drug combinations and employing drug sensitisers are potential means to combat DR. The evolution of drug combinations and polypharmacy on TI are reviewed. Notably, the novel binary weapon approach is introduced as a new opportunity to improve TI. This review emphasises the urgent need for a criterion to systematically evaluate drug safety and efficiency for practical implementation in the field.

Tetrandrine (TET) is a natural bis-benzylisoquinoline alkaloid isolated from Stephania species with a wide range of biological and pharmacologic activities; it mainly serves as an anti-inflammatory agent or antitumor adjuvant in clinical applications. However, limitations such as prominent hydrophobicity, severe off-target toxicity, and low absorption result in suboptimal therapeutic outcomes preventing its widespread adoption. Nanoparticles have proven to be efficient devices for targeted drug delivery since drug-carrying nanoparticles can be passively transported to the tumor site by the enhanced permeability and retention (EPR) effects, thus securing a niche in cancer therapies. Great progress has been made in nanocarrier construction for TET delivery due to their outstanding advantages such as increased water-solubility, improved biodistribution and blood circulation, reduced off-target irritation, and combinational therapy. Herein, we systematically reviewed the latest advancements in TET-loaded nanoparticles and their respective features with the expectation of providing perspective and guidelines for future research and potential applications of TET.

While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. SCIENTIFIC CONTRIBUTION: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.