ASDCD: antifungal synergistic drug combination database

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

Antifungal activity of selective serotonin reuptake inhibitors against Cryptococcus spp. and their possible mechanism of action

Fungal infections caused by Cryptococcus spp. pose a threat to health, especially in immunocompromised individuals. The available arsenal of drugs against cryptococcosis is limited, due to their toxicity and/or lack of accessibility in low-income countries, requiring more therapeutic alternatives. Selective serotonin reuptake inhibitors (SSRIs), through drug repositioning, are a promising alternative to broaden the range of new antifungals against Cryptococcus spp. This study evaluates the antifungal activity of three SSRIs, sertraline, paroxetine, and fluoxetine, against Cryptococcus spp. strains, as well as assesses their possible mechanism of action. Seven strains of Cryptococcus spp. were used. Sensitivity to SSRIs, fluconazole, and itraconazole was evaluated using the broth microdilution assay. The interactions resulting from combinations of SSRIs and azoles were investigated using the checkerboard assay. The possible action mechanism of SSRIs against Cryptococcus spp. was evaluated through flow cytometry assays. The SSRIs exhibited in vitro antifungal activity against Cryptococcus spp. strains, with minimum inhibitory concentrations ranging from 2 to 32 μg/mL, and had synergistic and additive interactions with azoles. The mechanism of action of SSRIs against Cryptococcus spp. involved damage to the mitochondrial membrane and increasing the production of reactive oxygen species, resulting in loss of cellular viability and apoptotic cell death. Fluoxetine also was able to cause significant damage to yeast DNA. These findings demonstrate the in vitro antifungal potential of SSRIs against Cryptococcus spp. strains.

Overcoming Drug Resistance in a Clinical C. albicans Strain Using Photoactivated Curcumin as an Adjuvant

The limited antifungal drugs available and the rise of multidrug-resistant Candida species have made the efforts to improve antifungal therapies paramount. To this end, our research focused on the effect of a combined treatment between chemical and photodynamic therapy (PDT) towards a fluconazole-resistant clinical Candida albicans strain. The co-treatment of PDT and curcumin in various doses with fluconazole (FLC) had an inhibitory effect on the growth of the FLC-resistant hospital strain of C. albicans in both difusimetric and broth microdilution methods. The proliferation of the cells was inhibited in the presence of curcumin at 3.125 µM and FLC at 41 µM concentrations. The possible involvement of oxidative stress was analyzed by adding menadione and glutathione as a prooxidant and antioxidant, respectively. In addition, we examined the photoactivated curcumin effect on efflux pumps, a mechanism often linked to drug resistance. Nile Red accumulation assays were used to evaluate efflux pumps activity through fluorescence microscopy and spectrofluorometry. The results showed that photoactivated curcumin at 3.125 µM inhibited the transport of the fluorescent substrate that cells usually expel, indicating its potential in combating drug resistance. Overall, the findings suggest that curcumin, particularly when combined with PDT, can effectively inhibit the growth of FLC-resistant C. albicans, addressing the challenge of yeast resistance to azole antifungals through upregulating multidrug transporters.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Combined Application of Tacrolimus with Cyproconazole, Hymexazol and Novel {2-(3-R-1H-1,2,4-triazol-5-yl)phenyl}amines as Antifungals: In Vitro Growth Inhibition and In Silico Molecular Docking Analysis to Fungal Chitin Deacetylase

Agents with antifungal activity play a vital role as therapeutics in health care, as do fungicides in agriculture. Effectiveness, toxicological profile, and eco-friendliness are among the properties used to select suitable substances. Furthermore, a steady supply of new agents with different modes of action is required to counter the well-known potential of human and phyto-pathogenic fungi to develop resistance against established antifungals. Here, we use an in vitro growth assay to investigate the activity of the calcineurin inhibitor tacrolimus in combination with the commercial fungicides cyproconazole and hymexazol, as well as with two earlier reported novel {2-(3-R-1H-1,2,4-triazol-5-yl)phenyl}amines, against the fungi Aspergillus niger, Colletotrichum higginsianum, Fusarium oxysporum and the oomycete Phytophthora infestans, which are notoriously harmful in agriculture. When tacrolimus was added in a concentration range from 0.25 to 25 mg/L to the tested antifungals (at a fixed concentration of 25 or 50 mg/L), the inhibitory activities were distinctly enhanced. Molecular docking calculations revealed triazole derivative 5, (2-(3-adamantan-1-yl)-1H-1,2,4-triazol-5-yl)-4-chloroaniline), as a potent inhibitor of chitin deacetylases (CDA) of Aspergillus nidulans and A. niger (AnCDA and AngCDA, respectively), which was stronger than the previously reported polyoxorin D, J075-4187, and chitotriose. The results are discussed in the context of potential synergism and molecular mode of action.

Knowledgebase of potential multifaceted solutions to antimicrobial resistance

Antimicrobial resistance (AMR), a top threat to global health, challenges preventive and treatment strategies of infections. AMR strains of microbial pathogens arise through multiple mechanisms. The underlying "antibiotic resistance genes" (ARGs) spread through various species by lateral gene transfer thereby causing global dissemination. Human methods also augment this process through inappropriate use, non-compliance to treatment schedule, and environmental waste. Worldwide significant efforts are being invested to discover novel therapeutic solutions for tackling resistant pathogens. Diverse therapeutic strategies have evolved over recent years. In this work we have developed a comprehensive knowledgebase by collecting alternative antimicrobial therapeutic strategies from literature data. Therapeutic strategies against bacteria, virus, fungus and parasites were extracted from PubMed literature using text mining. We have used a subjective (sentimental) approach for data mining new strategies, resulting in broad coverage of novel entities and subsequently add objective data like entity name (including IUPAC), potency, and safety information. The extracted data was organized in a freely accessible web platform, KOMBAT. The KOMBAT comprises 1104 Chemical compounds, 220 of newly identified antimicrobial peptides, 42 bacteriophages, 242 phytochemicals, 106 nanocomposites, and 94 novel entities for phototherapy. Entities tested and evaluated on AMR pathogens are included. We envision that this database will be useful for developing future therapeutics against AMR pathogens. The database can be accessed through http://kombat.igib.res.in/.

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records

Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large amounts of biomedical data. Existing computational approaches are often underpowered due to their reliance on our limited understanding of disease mechanisms. On the other hand, observable phenotypic inter-relationships among thousands of diseases often reflect their underlying shared genetic and molecular underpinnings, therefore can offer unique opportunities to design computational models to discover novel combinational therapies by automatically transferring knowledge among phenotypically related diseases. We developed a novel phenome-driven drug discovery system, named TuSDC, which leverages knowledge of existing drug combinations, disease comorbidities, and disease treatments of thousands of disease and drug entities extracted from over 31.5 million biomedical research articles using natural language processing techniques. TuSDC predicts combination pharmacotherapy by extracting representations of diseases and drugs using tensor factorization approaches. In external validation, TuSDC achieved an average precision of 0.77 for top ranked candidates, outperforming a state of art mechanism-based method for discovering drug combinations in treating hypertension. We evaluated top ranked anti-hypertension drug combinations using electronic health records of 84.7 million unique patients and showed that a novel drug combination hydrochlorothiazide-digoxin was associated with significantly lower hazards of subsequent hypertension as compared to the monotherapy hydrochlorothiazide alone (HR: 0.769, 95% CI [0.732, 0.807]) and digoxin alone (0.857, 95% CI [0.785, 0.936]). Data-driven informatics analyses reveal that the renin-angiotensin system is involved in the synergistical interactions of hydrochlorothiazide and digoxin on regulating hypertension. The prediction model's code with PyTorch version 1.5 is available at http://nlp.case.edu/public/data/TuSDC/.

CDCDB: A large and continuously updated drug combination database

In recent years, due to the complementary action of drug combinations over mono-therapy, the multiple-drugs for multiple-targets paradigm has received increased attention to treat bacterial infections and complex diseases. Although new drug combinations screening has benefited from experimental tests like automated high throughput screening, it is limited due to the large number of possible drug combinations. The task of drug combination screening can be streamlined through computational methods and models. Such models require up-to-date databases; however, existing databases are static and consist of the data collected at the time of their creation. This paper introduces the Continuous Drug Combination Database (CDCDB), a continuously updated drug combination database. The CDCDB includes over 40,795 drug combinations, of which 17,107 are unique combinations consisting of more than 4,129 individual drugs, curated from ClinicalTrials.gov, the FDA Orange Book^®, and patents. To create CDCDB, we use various methods, including natural language processing techniques, to improve the process of drug combination discovery, ensuring that our database can be used for drug synergy prediction. Website: https://icc.ise.bgu.ac.il/medical_ai/CDCDB/.

Measurement(s)	drug combination effect modeling • drug combination effect modeling
Technology Type(s)	Text mining • Clinical Trials Informatics System
Factor Type(s)	Medicine
Sample Characteristic - Organism	Homo sapiens

A plant mannose-binding lectin and fluconazole: key targets combination against Candida albicans

AIMS

This study aimed to evaluate the combined effect of a mannose-binding lectin Helja with fluconazole (FLC) on Candida albicans and to get insights about the joint action mechanism.

METHODS AND RESULTS

The fungal growth was assessed following the optical density at 630 nm. Fungal cell morphology and nucleus integrity were analyzed by flow cytometry and confocal laser scanning microscopy using Calcofluor White (CFW) and 4',6-diamidino-2-phenylindole (DAPI) staining, respectively. The basis of Helja+FLC action on cell wall and plasma membrane was analyzed using perturbing agents. The Helja+FLC combination exhibited an inhibitory effect of fungal growth about three times greater than the sum of both compounds separately and inhibited fungal morphological plasticity, an important virulence attribute associated with drug resistance. Cells treated with Helja+FLC showed morphological changes, nucleus disintegration and formation of multimera structures, leading to cell collapse.

CONCLUSIONS

Our findings indicate that the Helja+FLC combination exhibited a potent antifungal activity based on their simultaneous action on different microbial cell targets.

SIGNIFICANCE AND IMPACT OF STUDY

The combination of a natural protein with conventional drugs might be helpful for the design of effective therapeutic strategies against Candida, contributing to minimize the development of drug resistance and host cell toxicity.

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.

Heterogeneous information network and its application to human health and disease

The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.

Machine learning methods, databases and tools for drug combination prediction

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Application of Machine Learning Techniques in Drug-target Interactions Prediction

BACKGROUND

Drug-Target interactions are vital for drug design and drug repositioning. However, traditional lab experiments are both expensive and time-consuming. Various computational methods which applied machine learning techniques performed efficiently and effectively in the field.

RESULTS

The machine learning methods can be divided into three categories basically: Supervised methods, Semi-Supervised methods and Unsupervised methods. We reviewed recent representative methods applying machine learning techniques of each category in DTIs and summarized a brief list of databases frequently used in drug discovery. In addition, we compared the advantages and limitations of these methods in each category.

CONCLUSION

Every prediction model has both strengths and weaknesses and should be adopted in proper ways. Three major problems in DTIs prediction including the lack of nonreactive drug-target pairs data sets, over optimistic results due to the biases and the exploiting of regression models on DTIs prediction should be seriously considered.

Inferring Synergistic Drug Combinations Based on Symmetric Meta-Path in a Novel Heterogeneous Network

Combinatorial drug therapy is a promising way for treating cancers, which can reduce drug side effects and improve drug efficacy. However, due to the large-scale combinatorial space, it is difficult to quickly and effectively identify novel synergistic drug combinations for further implementing combinatorial drug therapy. The computational method of fusing multi-source knowledge is a time- and cost-efficient strategy to infer synergistic drug combinations for testing. However, for the existing computational methods of inferring synergistic drug combinations, it still remains a challenging to effectively combine multi-source information to achieve the desired results. Hence, in this study, we developed a novel Inference method of Synergistic Drug Combinations based on Symmetric Meta-Path (ISDCSMP), which can systematically and accurately prioritize synergistic drug combinations in a novel drug-target heterogeneous network integrating multi-source information. In the experiment, ISDCSMP outperformed the state-of-the-art methods in terms of AUC and precision on the benchmark dataset in five-fold cross validation. Moreover, we further illustrated performances of different ways for obtaining the combination coefficients, and analyzed the influences of the maximum meta-path length. The performances of various single meta-paths were described in five-fold cross validation. Finally, we confirmed the practical usefulness of ISDCSMP with the predicted novel synergistic drug combinations. The source code of ISDCSMP is available at https://github.com/KDDing/ISDCSMP.

Ketorolac-fluconazole: A New Combination Reverting Resistance in Candida albicans from Acute Myeloid Leukemia Patients on Induction Chemotherapy: In vitro Study

BACKGROUND AND OBJECTIVES

Candida albicans is a significant source of morbidity and mortality for patients with acute myeloid leukemia (AML). Prolonged use of fluconazole as empirical antifungal prophylaxis in AML patients leads to overexpression of efflux pump genes that resulted in the emergence of azole-resistant species. Consequently, the introduction of a new strategy to improve the management of C. albicans infections is an urgent need. Nonsteroidal anti-inflammatory drug (NSAID) ketorolac is associated with a reduction in cancer relapses. The present study was performed to investigate the use of ketorolac-fluconazole combination to reverse fluconazole resistance in C. albicans isolated from AML patients on induction chemotherapy.

PATIENTS AND METHODS

One hundred and seventy AML patients were evaluated. Fifty C. albicans were isolated and subjected to disc diffusion assay and broth microdilution for fluconazole alone and combined with different concentrations of ketorolac. Efflux pump gene (CDR1, CDR2, and MDR1) expressions were quantified by real-time PCR.

RESULTS

The tested ketorolac acted synergistically with fluconazole against resistant C. albicans with the minimum inhibitory concentration (MIC) of fluconazole decreased from >160 μg/mL to 0.3-1.25 μg/mL in (93.8%) of resistant isolates with fractional inhibitory concentration index (FICI) value of 0.25. The majority of the resistant isolates overexpressed CDR1 (71.1%) and MDR1 (60%).

CONCLUSION

Ketorolac-fluconazole in vitro combination would be a promising strategy for further clinical in vivo trials to overcome fluconazole resistance in AML patients on induction chemotherapy.

Antifungal Combination of Ethyl Acetate Extract of Poincianella pluviosa (DC.) L. P. Queiros Stem Bark With Amphotericin B in Cryptococcus neoformans

Cryptococcus neoformans is the leading cause of cryptococcosis, an invasive and potentially fatal infectious disease. Therapeutic failures are due to the increase in antifungal resistance, the adverse effects of drugs, and the unavailability of therapeutic regimens in low-income countries, which limit the treatment of cryptococcosis, increasing the morbidity and mortality associated with these infections. Thus, new antifungal drugs and innovative strategies for the cryptococcosis treatment are urgently needed. The aim of the present study was to evaluate the effect of ethyl acetate fraction (EAF) of Poincianella pluviosa stem bark on planktonic and biofilm mode of growth of C. neoformans. Furthermore, the interaction between the EAF and amphotericin B (AmB) was evaluated in vitro and in Galleria mellonella infection model. Minimal inhibitory concentrations (MICs) of EAF ranged from 125.0 to >1,000.0 μg/ml and >1,000.0 μg/ml for planktonic and sessile cells, respectively. The combination between EAF and AmB exhibited a synergistic fungicidal activity toward C. neoformans, with a fractional inhibitory concentration index (FICI) ranging from 0.03 to 0.06 and 0.08 to 0.28 for planktonic and sessile cells, respectively. Microscopy analyses of planktonic C. neoformans cells treated with EAF, alone or combined with AmB, revealed morphological and ultrastructural alterations, including loss of integrity of the cell wall and cell membrane detachment, suggesting leakage of intracellular content, reduction of capsule size, and presence of vacuoles. Moreover, EAF alone or combined with AmB prolonged the survival rate of C. neoformans-infected G. mellonella larvae. These findings indicate that P. pluviosa may be an important source of new compounds that can be used as a fungus-specific adjuvant for the treatment of cryptococcosis.

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming. Machine learning methods, especially deep learning, are widely applied to DTIs prediction. In this study, the main goal is to provide a comprehensive overview of deep learning-based DTIs prediction approaches. Here, we investigate the existing approaches from multiple perspectives. We explore these approaches to find out which deep network architectures are utilized to extract features from drug compound and protein sequences. Also, the advantages and limitations of each architecture are analyzed and compared. Moreover, we explore the process of how to combine descriptors for drug and protein features. Likewise, a list of datasets that are commonly used in DTIs prediction is investigated. Finally, current challenges are discussed and a short future outlook of deep learning in DTI prediction is given.

Current and promising pharmacotherapeutic options for candidiasis

Introduction: Candida spp. are commensal yeasts capable of causing infections such as superficial, oral, vaginal, or systemic infections. Despite medical advances, the antifungal pharmacopeia remains limited and the development of alternative strategies is needed.Areas covered: We discuss available treatments for Candida spp. infections, highlighting advantages and limitations related to pharmacokinetics, cytotoxicity, and antimicrobial resistance. Moreover, we present new perspectives to improve the activity of the available antifungals, discussing their immunomodulatory potential and advances on drug delivery carriers. New therapeutic approaches are presented including recent synthesized antifungal compounds (Enchochleated-Amphotericin B, tetrazoles, rezafungin, enfumafungin, manogepix and arylamidine); drug repurposing using a diversity of antibacterial, antiviral and non-antimicrobial drugs; combination therapies with different compounds or photodynamic therapy; and innovations based on nano-particulate delivery systems.Expert opinion: With the lack of novel drugs, the available assets must be leveraged to their best advantage through modifications that enhance delivery, efficacy, and solubility. However, these efforts are met with continuous challenges presented by microbes in their infinite plight to resist and survive therapeutic drugs. The pharmacotherapeutic options in development need to focus on new antimicrobial targets. The success of each antimicrobial agent brings strategic insights to the next phased approach in treatingCandida spp. infections.

Synergistic antifungal evaluation of over-the-counter antifungal creams with turmeric essential oil or Aloe vera gel against pathogenic fungi

BACKGROUND

The frequent incidence of fungal infection and widespread of antibiotic resistance are emergent concerns in public health. Hence, there is a need to harness the potential of natural bioactive compounds from plant towards treatment of fungal infection. Combination effect of antibiotic creams with natural products from plants is prospective strategy to produce new antifungal agent. This study therefore, revealed antifungal effect of combined Antifungal Creams (AFCs) with Turmeric Essential Oil (TEO) or Aloe vera Gel (AVG).

METHODS

Phytochemicals and bioactive compounds in TEO and AVG were revealed using GC-MS. Bioactive compounds in plant extracts were compared to known compounds in database library of National Institute of Standards and Technology (U.S.). Antifungal activity and synergistic effect of AFCs with TEO or AVG were carried out using agar well diffusion method.

RESULTS

Phenol, flavonoids, saponins, alkaloids, steroids, terpenoids and cardiac glycosides were present in TEO and AVG. GCMS revealed thirty-six (36) and eighteen (18) bioactive compounds in TEO and AVG, respectively. AFCs displayed zones of inhibition with values ranged from 5.0 to 14.3 mm, TEO was 5.0 to 11.0 mm and AVG was 8.0 to 11.7 mm against tested fungi. Minimum Inhibitory Concentration (MIC) by AFCs, TEO and AVG ranged from 1.25 to 10.0 mg/ml. Combinatory effects of AFCs with TEO or AVG revealed synergistic and indifferent properties.

CONCLUSION

Development of novel products using bioactive ingredients from plants with commercially available AFCs will serve as potential alternative therapy to cure dermatological infections with no side effects.

In vitro synergistic effects of fluoxetine and paroxetine in combination with amphotericin B against Cryptococcus neoformans

Cryptococcus neoformans is a yeast that mainly affects immunocompromised individuals and causes meningoencephalitis depending on the immune status of the host. The present study aimed to validate the efficacy of selective serotonin reuptake inhibitors, fluoxetine hydrochloride (FLH) and paroxetine hydrochloride (PAH), alone and in combination with amphotericin B (AmB) against C. neoformans. Susceptibility tests were conducted using the broth microdilution method and synergistic effects of combining FLH and PAH with AmB were analyzed using the checkerboard assay. Effects of minimum inhibitory concentration (MIC) and synergistic concentration were evaluated in biofilms by quantifying the biomass, measuring the viability by counting the colony-forming units (CFU/mL) and examining the size of the induced capsules. Cryptococcus neoformans was susceptible to FLH and PAH and the synergistic effect of FLH and PAH in combination with AmB reduced the MIC of AmB by up to 8-fold. The isolated substances and combination with AmB were able to reduce biofilm biomass and biofilm viability. In addition, FLH and PAH alone or in combination with AmB significantly decreased the size of the yeast capsules. Collectively, our results indicate the use of FLH and PAH as a promising prototype for the development of anti-cryptococcal drugs.

Combination Therapy to Treat Fungal Biofilm-Based Infections

An increasing number of people is affected by fungal biofilm-based infections, which are resistant to the majority of currently-used antifungal drugs. Such infections are often caused by species from the genera Candida, Aspergillus or Cryptococcus. Only a few antifungal drugs, including echinocandins and liposomal formulations of amphotericin B, are available to treat such biofilm-based fungal infections. This review discusses combination therapy as a novel antibiofilm strategy. More specifically, in vitro methods to discover new antibiofilm combinations will be discussed. Furthermore, an overview of the main modes of action of promising antibiofilm combination treatments will be provided as this knowledge may facilitate the optimization of existing antibiofilm combinations or the development of new ones with a similar mode of action.

DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy

Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.

Synergistic effects of ketamine and azole derivatives on Candida spp. resistance to fluconazole

The emergence of Candida spp. with resistance to antifungal molecules, mainly the azole class, is an increasing complication in hospitals around the globe. Aim: In the present research, we evaluated the synergistic effects of ketamine with two azole derivatives, itraconazole and fluconazole, on strains of Candida spp. to fluconazole. Materials & methods: The drug synergy was evaluated by quantifying the fractional inhibitory concentration index and by fluorescence microscopy and flow cytometry techniques. Results: Our achievements showed a synergistic effect between ketamine in addition to the two antifungal agents (fluconazole and itraconazole) against planktonic cells and biofilms of Candida spp. Conclusion: This combination promoted alteration of membrane integrity, generation of reactive oxygen species, damage to and DNA and externalization of phosphatidylserine.

Advances in computational approaches in identifying synergistic drug combinations

Accumulated empirical clinical experience, supported by animal or cell line models, has initiated efforts of predicting synergistic combinatorial drugs with more-than-additive effect compared with the sum of the individual agents. Aiming to construct better computational models, this review started from the latest updated data resources of combinatorial drugs, then summarized the reported mechanism of the known synergistic combinations from aspects of drug molecular and pharmacological patterns, target network properties and compound functional annotation. Based on above, we focused on the main in silico strategies recently published, covering methods of molecular modeling, mathematical simulation, optimization of combinatorial targets and pattern-based statistical/learning model. Future thoughts are also discussed related to the role of natural compounds, drug combination with immunotherapy and management of adverse effects. Overall, with particular emphasis on mechanism of action of drug synergy, this review may serve as a rapid reference to design improved models for combinational drugs.

Synergistic drug combinations prediction by integrating pharmacological data

There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.

CANCROX: a cross-species cancer therapy database

Cancer comprises a set of more than 200 diseases resulting from the uncontrolled growth of cells that invade tissues and organs, which can spread to other regions of the body. The types of cancer found in humans are also described in animal models, a fact that has raised the interest of the scientific community in comparative oncology studies. In this study, bioinformatics tools were used to implement a computational model that uses text mining and natural language processing to construct a reference database that relates human and canine genes potentially associated with cancer, defining genetic pathways and information about cancer and cancer therapies. The CANCROX reference database was constructed by processing the scientific literature and lists more than 1300 drugs and therapies used to treat cancer, in addition to over 10 000 combinations of these drugs, including 40 types of cancer. A user-friendly interface was developed that enables researchers to search for different types of information about therapies, drug combinations, genes and types of cancer. In addition, data visualization tools allow to explore and relate different drugs and therapies for the treatment of cancer, providing information for groups studying animal models, in this case the dog, as well as groups studying cancer in humans.

Machine Learning for Drug-Target Interaction Prediction

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.

Analysis of the Literature and Patents on Solid Dispersions from 1980 to 2015

Background: Solid dispersions are an effective formulation technique to improve the solubility, dissolution rate, and bioavailability of water-insoluble drugs for oral delivery. In the last 15 years, increased attention was focused on this technology. There were 23 marketed drugs prepared by solid dispersion techniques. Objective: This study aimed to report the big picture of solid dispersion research from 1980 to 2015. Method: Scientific knowledge mapping tools were used for the qualitative and the quantitative analysis of patents and literature from the time and space dimensions. Results: Western Europe and North America were the major research areas in this field with frequent international cooperation. Moreover, there was a close collaboration between universities and industries, while research collaboration in Asia mainly existed between universities. The model drugs, main excipients, preparation technologies, characterization approaches and the mechanism involved in the formulation of solid dispersions were analyzed via the keyword burst and co-citation cluster techniques. Integrated experimental, theoretical and computational tools were useful techniques for in silico formulation design of the solid dispersions. Conclusions: Our research provided the qualitative and the quantitative analysis of patents and literature of solid dispersions in the last three decades.

Ensemble Prediction of Synergistic Drug Combinations Incorporating Biological, Chemical, Pharmacological, and Network Knowledge

Combinatorial therapy may reduce drug side effects and improve drug efficacy, making combination therapy a promising strategy to treat complex diseases. However, in the existing computational methods, the natural properties and network knowledge of drugs have not been adequately and simultaneously considered, making it difficult to identify effective drug combinations. Computational methods that incorporate multiple sources of information (biological, chemical, pharmacological, and network knowledge) offer more opportunities to screen synergistic drug combinations. Therefore, we developed a novel Ensemble Prediction framework of Synergistic Drug Combinations (EPSDC) to accurately and efficiently predict drug combinations by integrating information from multiple-sources. EPSDC constructs feature vector of drug pair by concatenating different types of drug similarities, and then uses these groups in a feature-based base predictor. Next, transductive learning is applied on heterogeneous drug-target networks to achieve a network-based score for the drug pair. Finally, two types of ensemble rules are introduced to combine the feature-based score and the network-based score, and then potential drug combinations are prioritized. To demonstrate the effect of the ensemble rule, comprehensive experiments were conducted to compare single models and ensemble models. The experimental results indicated that our method outperformed the state-of-the-art method in five-fold cross validation and de novo prediction tests on the two benchmark datasets. We further analyzed the effect of maximum length of the meta-path and the impacts of different types of features. Moreover, the practical usefulness of our method was confirmed in the predicted novel drug combinations. The source code of EPSDC is available at https://github.com/KDDing/EPSDC.

Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination

The efficacy of pharmacological perturbation to the signaling transduction network depends on the network topology. However, whether and how signaling dynamics mediated by crosstalk contributes to the drug resistance are not fully understood and remain to be systematically explored. In this study, motivated by a realistic signaling network linked by crosstalk between EGF/EGFR/Ras/MEK/ERK pathway and HGF/HGFR/PI3K/AKT pathway, we develop kinetic models for several small networks with typical crosstalk modules to investigate the role of the architecture of crosstalk in inducing drug resistance. Our results demonstrate that crosstalk inhibition diminishes the response of signaling output to the external stimuli. Moreover, we show that signaling crosstalk affects the relative sensitivity of drugs, and some types of crosstalk modules that could yield resistance to the targeted drugs were identified. Furthermore, we quantitatively evaluate the relative efficacy and synergism of drug combinations. For the modules that are resistant to the targeted drug, we identify drug targets that can not only increase the relative drug efficacy but also act synergistically. In addition, we analyze the role of the strength of crosstalk in switching a module between drug-sensitive and drug-resistant. Our study provides mechanistic insights into the signaling crosstalk-mediated mechanisms of drug resistance and provides implications for the design of synergistic drug combinations to reduce drug resistance.

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

Motivation

While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.

Results

DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations.

Availability and implementation

DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy.

Contact

klambauer@bioinf.jku.at.

Supplementary information

Supplementary data are available at Bioinformatics online.

Lovastatin synergizes with itraconazole against planktonic cells and biofilms of Candida albicans through the regulation on ergosterol biosynthesis pathway

The increase of fungal infectious diseases and lack of safe and efficacious antifungal drugs result in the urgent need of new therapeutic strategies. Here, we repurposed the lovastatin (LOV) as a synergistic antifungal potentiator to itraconazole (ITZ) against Candida albicans planktonic cells and biofilms in vitro for the first time. Mutants from ergosterol biosynthesis pathway were employed and key gene expression profiles of ergosterol pathway were also measured. LOV single treatment was unable to inhibit C. albicans strains except the ERG3 and ERG11 double mutant. LOV and ITZ combination was capable of inhibiting the C. albicans planktonic cells and biofilms synergistically including the ITZ resistant mutants. The synergistic antifungal ability was stronger in either ERG11 or ERG3 dysfunctional mutants compared to wild type. The combination lost the synergistic activities in the ERG11 and ERG3 double mutant, while it was sensitive to LOV single treatment. The expression of HMG1, encoding HMG-CoA the target of LOV, was significantly upregulated in ERG11 and ERG3 double mutant strain by the treatment of the combination at 1.5 and 3 h. The combination also significantly increased the HMG1 expression in mutants from ergosterol pathway compared with wild type. The ERG11 and ERG3 gene expressions were upregulated by ITZ and its combination with LOV, but seemingly not by LOV single treatment after 1.5 and 3 h. The combination of LOV and ITZ on C. albicans planktonic cells and biofilms highlights its potential clinical practice especially against the azole drug-resistant mutants.

Reversal of Azole Resistance in Candida albicans by Sulfa Antibacterial Drugs

Invasive candidiasis presents an emerging global public health challenge due to the emergence of resistance to the frontline treatment options, such as fluconazole. Hence, the identification of other compounds capable of pairing with fluconazole and averting azole resistance would potentially prolong the clinical utility of this important group. In an effort to repurpose drugs in the field of antifungal drug discovery, we explored sulfa antibacterial drugs for the purpose of reversing azole resistance in Candida In this study, we assembled and investigated a library of 21 sulfa antibacterial drugs for their ability to restore fluconazole sensitivity in Candida albicans Surprisingly, the majority of assayed sulfa drugs (15 of 21) were found to exhibit synergistic relationships with fluconazole by checkerboard assay with fractional inhibitory concentration index (ΣFIC) values ranging from <0.0312 to 0.25. Remarkably, five sulfa drugs were able to reverse azole resistance in a clinically achievable range. The structure-activity relationships (SARs) of the amino benzene sulfonamide scaffold as antifungal agents were studied. We also identified the possible mechanism of the synergistic interaction of sulfa antibacterial drugs with azole antifungal drugs. Furthermore, the ability of sulfa antibacterial drugs to inhibit Candida biofilm by 40% in vitro was confirmed. In addition, the effects of sulfa-fluconazole combinations on Candida growth kinetics and efflux machinery were explored. Finally, using a Caenorhabditis elegans infection model, we demonstrated that the sulfa-fluconazole combination does possess potent antifungal activity in vivo, reducing Candida in infected worms by ∼50% compared to the control.

Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function

In recent years, an epidemic of the highly pathogenic avian influenza H7N9 virus has persisted in China, with a high mortality rate. To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase (NA) protein. RF-NA-Score is more accurate than RF-Score, with a root-mean-square error of 1.46, Pearson’s correlation coefficient of 0.707, and Spearman’s rank correlation coefficient of 0.707 in a 5-fold cross-validation study. The performance of RF-NA-Score in a docking-based virtual screening of NA inhibitors was evaluated with a dataset containing 281 NA inhibitors and 322 noninhibitors. Compared with other docking–rescoring virtual screening strategies, rescoring with RF-NA-Score significantly improved the efficiency of virtual screening, and a strategy that averaged the scores given by RF-NA-Score, based on the binding conformations predicted with AutoDock, AutoDock Vina, and LeDock, was shown to be the best strategy. This strategy was then applied to the virtual screening of NA inhibitors in the SPECS database. The 100 selected compounds were tested in an in vitro H7N9 NA inhibition assay, and two compounds with novel scaffolds showed moderate inhibitory activities. These results indicate that RF-NA-Score improves the efficiency of virtual screening for NA inhibitors, and can be used successfully to identify new NA inhibitor scaffolds. Scoring functions specific for other drug targets could also be established with the same method.

Antifungal Therapy: New Advances in the Understanding and Treatment of Mycosis

The high rates of morbidity and mortality caused by fungal infections are associated with the current limited antifungal arsenal and the high toxicity of the compounds. Additionally, identifying novel drug targets is challenging because there are many similarities between fungal and human cells. The most common antifungal targets include fungal RNA synthesis and cell wall and membrane components, though new antifungal targets are being investigated. Nonetheless, fungi have developed resistance mechanisms, such as overexpression of efflux pump proteins and biofilm formation, emphasizing the importance of understanding these mechanisms. To address these problems, different approaches to preventing and treating fungal diseases are described in this review, with a focus on the resistance mechanisms of fungi, with the goal of developing efficient strategies to overcoming and preventing resistance as well as new advances in antifungal therapy. Due to the limited antifungal arsenal, researchers have sought to improve treatment via different approaches, and the synergistic effect obtained by the combination of antifungals contributes to reducing toxicity and could be an alternative for treatment. Another important issue is the development of new formulations for antifungal agents, and interest in nanoparticles as new types of carriers of antifungal drugs has increased. In addition, modifications to the chemical structures of traditional antifungals have improved their activity and pharmacokinetic parameters. Moreover, a different approach to preventing and treating fungal diseases is immunotherapy, which involves different mechanisms, such as vaccines, activation of the immune response and inducing the production of host antimicrobial molecules. Finally, the use of a mini-host has been encouraging for in vivo testing because these animal models demonstrate a good correlation with the mammalian model; they also increase the speediness of as well as facilitate the preliminary testing of new antifungal agents. In general, many years are required from discovery of a new antifungal to clinical use. However, the development of new antifungal strategies will reduce the therapeutic time and/or increase the quality of life of patients.

Biomolecular Network-Based Synergistic Drug Combination Discovery

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

Highly chemoselective ligation of thiol- and amino-peptides on a bromomaleimide core

Application of a bromomaleimide core allows for the incorporation of three different peptides. The key reactions of the process are the selective stapling of both thiol- and amino-peptides on two different sites of the core. The thiol-peptide attacks and replaces the bromide whereas the amino-peptide attaches to the ene-position of the core revealing differential and selective reactivity. This platform will have further application in protein chemistry, multidrug presentation and vaccine preparation.

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.

Drug combinations represent a promising strategy for overcoming fungal drug resistance and treating complex diseases. There is an urgent need to establish powerful computational methods for systematic prediction of synergistic drug combination on a large scale. Based on the assumption that principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa, NLLSS was developed to predict potential synergistic drug combinations by integrating known synergistic drug combinations, unlabeled drug combinations, drug-target interactions, and drug chemical structures. NLLSS has obtained the reliable performance in the cross validation and experimental validations, which indicated that NLLSS has an excellent performance of identifying potential synergistic drug combinations. Out of 13 predicted antifungal synergistic drug combinations, 7 candidates were experimentally confirmed. It is anticipated that NLLSS would be an important and useful resource by providing a new strategy to identify potential synergistic antifungal combinations, explore new indications of existing drugs, and provide useful insights into the underlying molecular mechanisms of synergistic drug combinations.

Combinatorial strategies for combating invasive fungal infections

Invasive fungal infections are an important cause of human mortality and morbidity, particularly for immunocompromised populations. However, there remains a paucity of antifungal drug treatments available to combat these fungal pathogens. Further, antifungal compounds are plagued with problems such as host toxicity, fungistatic activity, and the emergence of drug resistance in pathogen populations. A promising therapeutic strategy to increase drug effectiveness and mitigate the emergence of drug resistance is through the use of combination drug therapy. In this review we describe the current arsenal of antifungals in medicine and elaborate on the benefits of combination therapy to expand our current antifungal drug repertoire. We examine those antifungal combinations that have shown potential against fungal pathogens and discuss strategies being employed to discover novel combination therapeutics, in particular combining antifungal agents with non-antifungal bioactive compounds. The findings summarized in this review highlight the promise of combinatorial strategies in combatting invasive mycoses.

Synergistic combinations of antifungals and anti-virulence agents to fight against Candida albicans

Candida albicans, one of the pathogenic Candida species, causes high mortality rate in immunocompromised and high-risk surgical patients. In the last decade, only one new class of antifungal drug echinocandin was applied. The increased therapy failures, such as the one caused by multi-drug resistance, demand innovative strategies for new effective antifungal drugs. Synergistic combinations of antifungals and anti-virulence agents highlight the pragmatic strategy to reduce the development of drug resistant and potentially repurpose known antifungals, which bypass the costly and time-consuming pipeline of new drug development. Anti-virulence and synergistic combination provide new options for antifungal drug discovery by counteracting the difficulty or failure of traditional therapy for fungal infections.

Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives

The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.

Drug-target interaction prediction: databases, web servers and computational models

Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.

Target-independent prediction of drug synergies using only drug lipophilicity

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds (“drugs”) previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms.