Using response surface models to analyze drug combinations

Towards unbiased interpretations of interactive effects in ecotoxicological studies

Ecotoxicological research has increasingly focused on the interactive effects of chemical mixtures on biological models, emphasising additive, synergistic, or antagonistic interactions. However, these combination studies often test chemicals at unique concentrations (e.g. x:y), limiting our understanding of the effects across the full spectrum of possible combinations. Evidence from human toxicology suggests that interactive effects among chemicals can vary significantly with total concentration (e.g. x:y vs. 2x:2y), their ratio (e.g. x:2y vs. 2x:y), and the magnitude of the tested effect (e.g. LC10vs. LC50). Our non-exhaustive review of studies on binary mixtures in bee ecotoxicology reveals that such parameters are frequently neglected. Of the 60 studies we examined, only two utilised multiple total concentrations and ratios, thus exploring a broad range of possible combinations. In contrast, 26 studies tested only a single concentration of each chemical, resulting in incomplete interpretations of the potential interactive effects. Other studies utilised various concentrations and/or ratios but failed to capture a broad spectrum of possible combinations. We also discuss potential discrepancies in interactive effects based on different metrics and exposure designs. We advocate for future ecotoxicological studies to investigate a wider spectrum of chemical combinations, including various concentrations and ratios, and to address different levels of effects.

Drugtamer-PROTAC Conjugation Strategy for Targeted PROTAC Delivery and Synergistic Antitumor Therapy

Proteolysis-targeting chimeras (PROTACs) have emerged as a promising strategy for targeted protein degradation and drug discovery. To overcome the inherent limitations of conventional PROTACs, an innovative drugtamer-PROTAC conjugation approach is developed to enhance tumor targeting and antitumor potency. Specifically, a smart prodrug is designed by conjugating "drugtamer" to a nicotinamide phosphoribosyltransferase (NAMPT) PROTAC using a tumor microenvironment responsible linker. The "drugtamer" consists of fluorouridine nucleotide and DNA-like oligomer. Compared to NAMPT PROTAC and the combination of PROTAC + fluorouracil, the designed prodrug AS-2F-NP demonstrates superior tumor targeting, efficient cellular uptake, improved in vivo potency and reduced side effects. This study provides a promising strategy for the precise delivery of PROTAC and synergistic antitumor agents.

Interactions of plumbagin with five common antibiotics against Staphylococcus aureus in vitro

Staphylococcus aureus is the main culprit, causing a variety of severe clinical infections. At the same time, clinics are also facing the severe situation of antibiotic resistance. Therefore, effective strategies to address this problem may include expanding the antimicrobial spectrum by exploring alternative sources of drugs or delaying the development of antibiotic resistance through combination therapy so that existing antibiotics can continue to be used. Plumbagin (PLU) is a phytochemical that exhibits antibacterial activity. In the present study, we investigated the in vitro antibacterial activity of PLU. We selected five antibiotics with different mechanisms and inhibitory activities against S. aureus to explore their interaction with the combination of PLU. The interaction of combinations was evaluated by the Bliss independent model and visualized through response surface analysis. PLU exhibited potent antibacterial activity, with half maximal inhibitory concentration (IC50) and minimum inhibitory concentration (MIC) values against S. aureus of 1.73 μg/mL and 4 μg/mL, respectively. Synergism was observed when PLU was combined with nitrofurantoin (NIT), ciprofloxacin (CPR), mecillinam (MEC), and chloramphenicol (CHL). The indifference of the trimethoprim (TMP)-PLU pairing was demonstrated across the entire dose-response matrix, but significant synergy was observed within a specific dose region. In addition, no antagonistic interactions were indicated. Overall, PLU is not only a promising antimicrobial agent but also has the potential to enhance the growth-inhibitory activity of some antibiotics against S. aureus, and the use of the interaction landscape, along with the dose-response matrix, for analyzing and quantifying combination results represents an improved approach to comprehending antibacterial combinations.

Systematic review of computational methods for drug combination prediction

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.

Data vignettes for the application of response surface models in drug combination analysis

This data set contains the data used in Twarog et al. (2021) to examine the robustness and utility of response surface models in drug combination analysis. It includes simulated experimental data for the evaluation of traditional index methods, as well as a processed library of interaction metrics evaluated on the Merck OncoPolyPharmacology Screen (O'Neil et al., 2016), the scripts used to implement those metrics on all tested combinations in that screen, and scripts to evaluate the performance of those metrics in comparison with real-world mechanistic classifications. Finally, the data set includes data from several published and unpublished drug combination experiments, and scripts which allow the analyses of those experiments to be replicated and applied to new data.