Identification of unique synergistic drug combinations associated with downexpression of survivin in a preclinical breast cancer model system

Synthesis, crystal structure, biological and docking studies of 5-hydroxy-2-{[(2-methylpropyl)iminio]methyl}phenolate

Background: Schiff base compounds are potential drugs.Results: A Schiff base compound prepared by condensing 2,4-dihydroxy benzaldehyde and isobutylamine was characterized for structure, thermal, physicochemical and biological properties. The keto-enol tautomerism and azomethine functionality enhances electron delocaliZation and biological activity. The compound showed good antibacterial and antifungal activity at 40 μg/ml against bacteria such as Escherichia coli and Staphylococcus aureus and fungi like Candida albicans and Candida tropicalis. The docking study exhibits a moderate binding affinity for the GyrB protein in E. coli with a binding energy of -4.26 kcal/mol.Conclusion: The compound exhibits enhanced biological activity and suppression of cell growth at concentrations as low as 30 μg/ml. The IC50 for MFC-7 was found to be 41.5 μg/ml.

Nanocarriers overcoming biological barriers induced by multidrug resistance of chemotherapeutics in 2D and 3D cancer models

Multidrug resistance (MDR) is currently a big challenge in cancer therapy and limits its success in several patients. Tumors use the MDR mechanisms to colonize the host and reduce the efficacy of chemotherapeutics that are injected as single agents or combinations. MDR mechanisms are responsible for inactivation of drugs and formbiological barriers in cancer like the drug efflux pumps, aberrant extracellular matrix, hypoxic areas, altered cell death mechanisms, etc. Nanocarriers have some potential to overcome these barriers and improve the efficacy of chemotherapeutics. In fact, they are versatile and can deliver natural and synthetic biomolecules, as well as RNAi/DNAi, thus providing a controlled release of drugs and a synergistic effect in tumor tissues. Biocompatible and safe multifunctional biopolymers, with or without specific targeting molecules, modify the surface and interface properties of nanocarriers. These modifications affect the interaction of nanocarriers with cellular models as well as the selection of suitable models for in vitro experiments. MDR cancer cells, and particularly their 2D and 3D models, in combination with anatomical and physiological structures of tumor tissues, can boost the design and preparation of nanomedicines for anticancer therapy. 2D and 3D cancer cell cultures are suitable models to study the interaction, internalization, and efficacy of nanocarriers, the mechanisms of MDR in cancer cells and tissues, and they are used to tailor a personalized medicine and improve the efficacy of anticancer treatment in patients. The description of molecular mechanisms and physio-pathological pathways of these models further allow the design of nanomedicine that can efficiently overcome biological barriers involved in MDR and test the activity of nanocarriers in 2D and 3D models of MDR cancer cells.

Co-Encapsulation of l-Asparaginase and Etoposide in Dextran Nanoparticles for Synergistic Effect in Chronic Myeloid Leukemia Cells

Co-encapsulation of multiple therapeutic drugs in a single nanocarrier has the potential to enable synergistic interactions, increase drug efficacy, and reduce side effects. The enzyme l-asparaginase and the small molecule drug etoposide have a known synergistic effect against selected cancer types. However, both drugs differ significantly in size, molecular weight, and solubility, which often results in challenges when a simultaneous delivery is required. In this study, we present the co-encapsulation of a large hydrophilic enzyme l-asparaginase and the small hydrophobic drug etoposide into a biodegradable, biocompatible, and acid-responsive dextran-based nanoparticle system. These dual drug-loaded nanoparticles show an excellent cellular uptake in chronic myeloid leukemia (CML) K562 cells and a stepwise release of the cytotoxic payloads in a pH-dependent manner. In activity tests, the dual drug-loaded formulation has shown a significant effect on cell viability (down to 31%) compared to those incubated only with l-asparaginase (92%) or etoposide (82%) at a particle concentration of 125 μg∙mL^-1. These results show that the simultaneous co-delivery of these two drugs in K562 cells leads to synergistic cytotoxicity, indicating a great potential for the treatment of CML.

Bipartite network models to design combination therapies in acute myeloid leukaemia

Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.

Identifying effective drug combinations to treat cancer is a challenging task, either experimentally or computationally. Here, the authors develop a bipartite network modelling approach to propose drug combination strategies in acute myeloid leukaemia using patient and cell line drug screening data.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

Modified gaphene oxide (GO) particles in peptide hydrogels: a hybrid system enabling scheduled delivery of synergistic combinations of chemotherapeutics

The scheduled delivery of synergistic drug combinations is increasingly recognized as highly effective against advanced solid tumors. Of particular interest are composite systems that release a sequence of drugs with defined kinetics and molar ratios to enhance therapeutic effect, while minimizing the dose to patients. In this work, we developed a homogeneous composite comprising modified graphene oxide (GO) nanoparticles embedded in a Max8 peptide hydrogel, which provides controlled kinetics and molar ratios of release of doxorubicin (DOX) and gemcitabine (GEM). First, modified GO nanoparticles (tGO) were designed to afford high DOX loading and sustained release (18.9% over 72 h and 31.4% over 4 weeks). Molecular dynamics simulations were utilized to model the mechanism of DOX loading as a function of surface modification. In parallel, a Max8 hydrogel was developed to release GEM with faster kinetics and achieve a 10-fold molar ratio to DOX. The selected DOX/tGO nanoparticles were suspended in a GEM/Max8 hydrogel matrix, and the resulting composite was tested against a triple negative breast cancer cell line, MDA-MB-231. Notably, the composite formulation afforded a combination index of 0.093 ± 0.001, indicating a much stronger synergism compared to the DOX-GEM combination co-administered in solution (CI = 0.396 ± 0.034).

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

Current Methods for Quantifying Drug Synergism

The effectiveness of drug combinations for treatment of a variety of complex diseases is well established. "Drug cocktail" treatments are often prescribed to improve the overall efficacy, decrease toxicity, alter pharmacodynamics, etc in an overall treatment strategy. Specifically, if when combined, drugs interact in some way that causes the total effect to be greater than that predicted by their individual potencies, then drugs are considered synergistic. While there are established ways to quantify the impact of drug combinations clinically, it is an open challenge to quantitatively summarize a synergistic interaction. In this paper, we discuss an overview of the current statistical and mathematical methods for the study of drug combination effects, especially drug synergy quantification (where the interaction effect is not just detected, but quantified according to its magnitude). We first introduce two popular reference models for testing to null hypothesis of non-interaction for a combination, including the Bliss independence model and the Loewe additivity model. Then we discuss several methods for quantifying drug synergism. The advantages and disadvantages with these methods are also provided, and finally, we discuss important next directions in this area.

The potential of histone deacetylase inhibitors in breast cancer therapy

SUMMARY 

Breast cancer is the second leading cause of cancer-related mortality in women. Despite improvements in prevention, detection and treatment, breast cancer will be responsible for nearly 40,000 deaths in 2014. The function of histone deacetylases (HDACs) and their potential as therapeutic targets has become an area of intense investigation and small molecule inhibitors of HDACs (HDACi) are now being investigated as potential chemotherapeutics for breast cancer. In addition to altering chromatin structure through stabilization of histone acetylation, HDACi treatment induces the accumulation of acetylated isoforms of many nonhistone proteins, altering their structure and function. These structural changes influence protein–protein interactions and cellular processes including cell cycle arrest, apoptosis, autophagy, induction of reactive oxygen species and mitotic catastrophe. While the usefulness of these compounds as single agents for treatment of breast cancer is still under investigation, cotreatment with other therapies is being evaluated in a number of clinical trials.

Profile of panobinostat and its potential for treatment in solid tumors: an update

The histone deacetylase (HDAC) inhibitors have emerged as novel therapies for cancer. Panobinostat (LBH 589, Novartis Pharmaceuticals) is a pan-deacetylase inhibitor that is being evaluated in both intravenous and oral formulations across multiple tumor types. Comparable to the other HDACs, panobinostat leads to hyperacetylation of histones and other intracellular proteins, allowing for the expression of otherwise repressed genes, leading to inhibition of cellular proliferation and induction of apoptosis in malignant cells. Panobinostat, analogous to other HDAC inhibitors, also induces apoptosis by directly activating cellular death receptor pathways. Preclinical data suggests that panobinostat has inhibitory activity at nanomolar concentrations and appears to be the most potent clinically available HDAC inhibitor. Here we review the current status of panobinostat and discuss its role in the treatment of solid tumors.