Advances in computational approaches in identifying synergistic drug combinations

Combining treatments for migraine prophylaxis: the state-of-the-art

Combination treatments for migraine prophylaxis present a promising approach to addressing the diverse and complex mechanisms underlying migraine. This review explores the potential of combining oral conventional prophylactics, onabotulinumtoxin A, monoclonal antibodies (mAbs) targeting the calcitonin gene-related peptide (CGRP) pathway, and small molecule CGRP receptor antagonists (gepants). Among the most promising strategies, dual CGRP inhibition through mAbs and gepants may enhance efficacy by targeting both the CGRP peptide and its receptor, while the combination of onabotulinumtoxin A with CGRP treatments offers synergistic pain relief. Oral non-CGRP treatments, which are accessible and often prescribed for patients with comorbid conditions, provide an affordable and practical option in combination regimens. Despite the potential of these combinations, there is a lack of evidence to support their widespread inclusion in clinical guidelines. The high cost of certain combinations, such as onabotulinumtoxin A with a CGRP mAb or dual anti-CGRP mAbs, presents feasibility challenges. Further large-scale trials are needed to establish safe and effective combination protocols and solidify their role in clinical practice, particularly for treatment-resistant patients.

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases

Combination therapy is an important direction of continuous exploration in the field of medicine, with the core goals of improving treatment efficacy, reducing adverse reactions, and optimizing clinical outcomes. Machine learning technology holds great promise in improving the prediction of drug synergy combinations. However, most studies focus on single disease-oriented collaborative predictive models or involve excessive feature categories, making it challenging to predict the majority of new drugs. To address these challenges, the DrugSK comprehensive model was developed, which utilizes SMILES-BERT to extract structural information from 3492 drugs and trains on reactions from 48,756 drug combinations. DrugSK is an integrated learning model capable of predicting interactions among various drug categories. First, the primary learner is trained from the initial data set. Random forest, support vector machine, and XGboost model are selected as primary learners and logistic regression as secondary learners. A new data set is then "generated" to train level 2 learners, which can be thought of as a prediction for each model. Finally, the results are filtered using logistic regression. Furthermore, the combination of the new antibacterial drug Drafloxacin with other antibacterial agents was tested. The synergistic effect of Drafloxacin and Isavuconazonium in the fight against Candida albicans has been confirmed, providing enlightenment for the clinical treatment of skin infection. DrugSK's prediction is accurate in practical application and can also predict the probability of the outcome. In addition, the tendency of Drafloxacin and antifungal drugs to be synergistic was found. The development of DrugSK will provide a new blueprint for predicting drug combination synergies.

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug-drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein-protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein-protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.

De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder

The modulating effect of chemical compounds and therapeutics on gene transcription is well-reported and has been intensively studied for both clinical and research purposes. Emerging research points toward the utility of drug-induced transcriptional alterations in de novo molecular design and highlights the idea of phenotype-matching an expression signature of interest to the structures being designed. In this work, we build an autoencoder-based generative model, BiCEV, around this concept. Our generative autoencoder has demonstrably generated a set of new molecules from gene expression input with notable validity (96%), uniqueness (98%), and internal diversity (0.77). Further, we attempted to validate BiCEV by testing the model on gene-knockdown profiles and combined signatures of synergistic drug pairs. From these investigations, we found the designed structures to be consistently high in collective quality. However, when their similarities to the supposed functional equivalents as determined by shared targets were considered, the findings were somewhat mixed. In spite of this, we believe the generative model merits further development in conjunction with in vitro corroboration to lend itself to being an assistive tool for drug discovery experts, particularly to support the initial stages of hit identification and lead optimization.

Prediction of Synergistic Antibiotic Combinations by Graph Learning

Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem. However, it is difficult for us to find effective antibiotic combinations in the vast chemical space. Herein, we propose a graph learning framework to predict synergistic antibiotic combinations. In this model, a network proximity method combined with network propagation was used to quantify the relationships of drug pairs, and we found that synergistic antibiotic combinations tend to have smaller network proximity. Therefore, network proximity can be used for building an affinity matrix. Subsequently, the affinity matrix was fed into a graph regularization model to predict potential synergistic antibiotic combinations. Compared with existing methods, our model shows a better performance in the prediction of synergistic antibiotic combinations and interpretability.

The Synergistic Effects of 5-Aminosalicylic Acid and Vorinostat in the Treatment of Ulcerative Colitis

Background: The drug 5-aminosalicylic acid (5-ASA) is the first-line therapy for the treatment of patients with mild-to-moderate ulcerative colitis (UC). However, in some cases, 5-ASA cannot achieve the desired therapeutic effects. Therefore, patients have to undergo therapies that include corticosteroids, monoclonal antibodies or immunosuppressants, which are expensive and may be accompanied by significant side effects. Synergistic drug combinations can achieve greater therapeutic effects than individual drugs while contributing to combating drug resistance and lessening toxic side effects. Thus, in this study, we sought to identify synergistic drugs that can act synergistically with 5-ASA. Methods: We started our study with protein-metabolite analysis based on peroxisome proliferator-activated receptor gamma (PPARG), the therapeutic target of 5-ASA, to identify more additional potential drug targets. Then, we further evaluated the possibility of their synergy with PPARG by integrating Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis, pathway-pathway interaction analysis, and semantic similarity analysis. Finally, we validated the synergistic effects with in vitro and in vivo experiments. Results: The combination of 5-ASA and vorinostat (SAHA) showed lower toxicity and mRNA expression of p65 in human colonic epithelial cell lines (Caco-2 and HCT-116), and more efficiently alleviated the symptoms of dextran sulfate sodium (DSS)-induced colitis than treatment with 5-ASA and SAHA alone. Conclusion: SAHA can exert effective synergistic effects with 5-ASA in the treatment of UC. One possible mechanism of synergism may be synergistic inhibition of the nuclear factor kappa B (NF-kB) signaling pathway. Moreover, the metabolite-butyric acid may be involved.

An In Silico Method for Predicting Drug Synergy Based on Multitask Learning

To make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein-protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug-target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with relation through proteins. In cross-validation experiments, DSML improved the ability to predict drug synergy. Moreover, the reconstruction of drug-target interactions and the incorporation of multisource knowledge significantly improved drug combination predictions by a large margin. The potential drug combinations predicted by DSML demonstrate its ability to predict drug synergy.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

H-RACS: a handy tool to rank anti-cancer synergistic drugs

Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug synergy. Yet they normally require the drug-cell treatment results as an essential input, thus exclude the possibility to pre-screen those unexplored drugs without cell treatment profiling. Based on the largest dataset of 33,574 combinational scenarios, we proposed a handy webserver, H-RACS, to overcome the above problems. Being loaded with chemical structures and target information, H-RACS can recommend potential synergistic pairs between candidate drugs on 928 cell lines of 24 prevalent cancer types. A high model performance was achieved with AUC of 0.89 on independent combinational scenarios. On the second independent validation of DREAM dataset, H-RACS obtained precision of 67% among its top 5% ranking list. When being tested on new combinations and new cell lines, H-RACS showed strong extendibility with AUC of 0.84 and 0.81 respectively. As the first online server freely accessible at http://www.badd-cao.net/h-racs, H-RACS may promote the pre-screening of synergistic combinations for new chemical drugs on unexplored cancers.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

A Machine Learning Method for Drug Combination Prediction

Drug combination is now a hot research topic in the pharmaceutical industry, but experiment-based methodologies are extremely costly in time and money. Many computational methods have been proposed to address these problems by starting from existing drug combinations. However, in most cases, only molecular structure information is included, which covers too limited a set of drug characteristics to efficiently screen drug combinations. Here, we integrated similarity-based multifeature drug data to improve the prediction accuracy by using the neighbor recommender method combined with ensemble learning algorithms. By conducting feature assessment analysis, we selected the most useful drug features and achieved 0.964 AUC in the ensemble models. The comparison results showed that the ensemble models outperform traditional machine learning algorithms such as support vector machine (SVM), naïve Bayes (NB), and logistic regression (GLM). Furthermore, we predicted 7 candidate drug combinations for a specific drug, paclitaxel, and successfully verified that the two of the predicted combinations have promising effects.

Synergisms of genome and metabolism stabilizing antitumor therapy (GMSAT) in human breast and colon cancer cell lines: a novel approach to screen for synergism

BACKGROUND

Despite an improvement of prognosis in breast and colon cancer, the outcome of the metastatic disease is still severe. Microevolution of cancer cells often leads to drug resistance and tumor-recurrence. To target the driving forces of the tumor microevolution, we focused on synergistic drug combinations of selected compounds. The aim is to prevent the tumor from evolving in order to stabilize disease remission. To identify synergisms in a high number of compounds, we propose here a three-step concept that is cost efficient, independent of high-throughput machines and reliable in its predictions.

METHODS

We created dose response curves using MTT- and SRB-assays with 14 different compounds in MCF-7, HT-29 and MDA-MB-231 cells. In order to efficiently screen for synergies, we developed a screening tool in which 14 drugs were combined (91 combinations) in MCF-7 and HT-29 using EC25 or less. The most promising combinations were verified by the method of Chou and Talalay.

RESULTS

All 14 compounds exhibit antitumor effects on each of the three cell lines. The screening tool resulted in 19 potential synergisms detected in HT-29 (20.9%) and 27 in MCF-7 (29.7%). Seven of the top combinations were further verified over the whole dose response curve, and for five combinations a significant synergy could be confirmed. The combination Nutlin-3 (inhibition of MDM2) and PX-478 (inhibition of HIF-1α) could be confirmed for all three cell lines. The same accounts for the combination of Dichloroacetate (PDH activation) and NHI-2 (LDH-A inhibition). Our screening method proved to be an efficient tool that is reliable in its projections.

CONCLUSIONS

The presented three-step concept proved to be cost- and time-efficient with respect to the resulting data. The newly found combinations show promising results in MCF-7, HT-29 and MDA-MB231 cancer cells.

Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization

Drug combinations may reduce toxicity and increase therapeutic efficacy, offering a promising strategy to conquer multiple complex diseases. However, due to large-scale combinatorial space, it remains challenging to identify effective combinations. Although many computational methods have focused on predicting drug synergy to reduce combinatorial space, they fail to effectively consider multiple sources of important knowledge. Thus, it is necessary to propose a computational method that can exploit useful information to predict drug synergy. Here, we developed a computational method to predict drug synergy based on graph co-regularization, named DSGCR. By incorporating drug-target network patterns, pharmacological patterns, and prior knowledge of drug combinations, DSGCR performs predictions of synergistic drug combinations. Compared to several existing methods, DSGCR achieves superior performance in predicting drug synergy in terms of various metrics via cross-validation. Additionally, we analyzed the importance of various sources of drug knowledge concerning three DSGCR's scenarios. Finally, the potential of DSGCR to score drug synergy was confirmed by three predicted synergistic drug combinations.

An omics perspective on drug target discovery platforms

The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

Rational Design of Multitarget-Directed Ligands: Strategies and Emerging Paradigms

Due to the complexity of multifactorial diseases, single-target drugs do not always exhibit satisfactory efficacy. Recently, increasing evidence indicates that simultaneous modulation of multiple targets may improve both therapeutic safety and efficacy, compared with single-target drugs. However, few multitarget drugs are on market or in clinical trials, despite the best efforts of medicinal chemists. This article discusses the systematic establishment of target combination, lead generation, and optimization of multitarget-directed ligands (MTDLs). Moreover, we analyze some MTDLs research cases for several complex diseases in recent years and the physicochemical properties of 117 clinical multitarget drugs, with the aim to reveal the trends and insights of the potential use of MTDLs.

Optimal control nodes in disease-perturbed networks as targets for combination therapy

Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.

Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.

Searching Synergistic Dose Combinations for Anticancer Drugs

Recent development has enabled synergistic drugs in treating a wide range of cancers. Being highly context-dependent, however, identification of successful ones often requires screening of combinational dose on different testing platforms in order to gain the best anticancer effects. To facilitate the development of effective computational models, we reviewed the latest strategy in searching optimal dose combination from three perspectives: (1) mainly experimental-based approach; (2) Computational-guided experimental approach; and (3) mainly computational-based approach. In addition to the introduction of each strategy, critical discussion of their advantages and disadvantages were also included, with a strong focus on the current applications and future improvements.

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.

The Prediction of Drug-Disease Correlation Based on Gene Expression Data

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.