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The co-occurrence of cancer and heart failure (HF) represents a significant clinical drawback as each disease interferes with the treatment of the other. In addition to shared risk factors, a growing body of experimental and clinical evidence reveals numerous commonalities in the biology underlying both pathologies. Inflammation emerges as a common hallmark for both diseases as it contributes to the initiation and progression of both HF and cancer. Under stress, malignant and cardiac cells change their metabolic preferences to survive, which makes these metabolic derangements a great basis to develop intersection strategies and therapies to combat both diseases. Furthermore, genetic predisposition and clonal haematopoiesis are common drivers for both conditions and they hold great clinical relevance in the context of personalized medicine. Additionally, altered angiogenesis is a common hallmark for failing hearts and tumours and represents a promising substrate to target in both diseases. Cardiac cells and malignant cells interact with their surrounding environment called stroma. This interaction mediates the progression of the two pathologies and understanding the structure and function of each stromal component may pave the way for innovative therapeutic strategies and improved outcomes in patients. The interdisciplinary collaboration between cardiologists and oncologists is essential to establish unified guidelines. To this aim, pre-clinical models that mimic the human situation, where both pathologies coexist, are needed to understand all the aspects of the bidirectional relationship between cancer and HF. Finally, adequately powered clinical studies, including patients from all ages, and men and women, with proper adjudication of both cancer and cardiovascular endpoints, are essential to accurately study these two pathologies at the same time.

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference.

Exposure to nanoparticles (NPs) in pregnancy is increasingly linked to adverse effects on embryo‐fetal development and health later in life. However, the developmental toxicity mechanisms of NPs are largely unknown, in particular potential effects on the placental secretome, which orchestrates many developmental processes pivotal for pregnancy success. This study demonstrates extensive material‐ and pregnancy stage‐specific deregulation of placental signaling from a single exposure of human placental explants to physiologically relevant concentrations of engineered (silica (SiO2) and titanium dioxide (TiO2) NPs) and environmental NPs (diesel exhaust particles, DEPs). This includes a multitude of secreted inflammatory, vascular, and endocrine placental factors as well as extracellular vesicle (EV)‐associated proteins. Moreover, conditioned media (CM) from NP‐exposed explants induce pronounced anti‐angiogenic and anti‐vasculogenic effects, while early neurodevelopmental processes are only marginally affected. These findings underscore the potential of metal oxide NPs and DEPs for widespread interference with the placental secretome and identify vascular morphogenesis as a sensitive outcome for the indirect developmental toxicity of different NPs. Overall, this work has profound implications for the future safety assessment of NPs for industrial, commercial, or medical applications in pregnancy, which should consider placenta‐mediated toxicity by holistic secretomics approaches to ensure the development of safe nanotechnologies. [ABSTRACT FROM AUTHOR]

We describe a protocol for the discovery of synergistic drug combinations for the treatment of disease. Synergistic drug combinations lead to the use of drugs at lower doses, which reduces side effects and can potentially lead to reduced drug resistance, while being clinically more effective than the individual drugs. To cope with the extremely large search space for these combinations, we developed an efficient combinatorial drug screening method called the Feedback System Control (FSC) technique. Starting with a broad selection of drugs, the method follows an iterative approach of experimental testing in a relevant bioassay and analysis of the results by FSC. First, the protocol uses a cell viability assay to generate broad dose-response curves to assess the efficacy of individual compounds. These curves are then used to guide the dosage input of each drug to be tested in combination. Data from applied drug combinations are input into the differential evolution (DE) algorithm, which predicts new combinations to be tested in vitro. This process identifies optimal drug-dose combinations, while saving orders of magnitude in experimental effort. The complete optimization process is estimated to take ∼4 weeks. FSC does not require insight into the disease mechanism, and it has therefore been applied to find combination therapies for many different pathologies, including cancer and infectious diseases, and it has also been used in organ transplantation.

A major key to improvement of cancer therapy is the combination of drugs. Mixing drugs that already exist on the market may offer an attractive alternative. Here we report on a new model-based streamlined feedback system control (s-FSC) method, based on a design of experiment approach, for rapidly finding optimal drug mixtures with minimal experimental effort. We tested combinations in an in vitro assay for the viability of a renal cell adenocarcinoma (RCC) cell line, 786-O. An iterative cycle of in vitro testing and s-FSC analysis was repeated a few times until an optimal low dose combination was reached. Starting with ten drugs that target parallel pathways known to play a role in the development and progression of RCC, we identified the best overall drug combination, being a mixture of four drugs (axitinib, erlotinib, dasatinib and AZD4547) at low doses, inhibiting 90% of cell viability. The removal of AZD4547 from the optimized drug combination resulted in 80% of cell viability inhibition, while still maintaining the synergistic interaction. These optimized drug combinations were significantly more potent than monotherapies of all individual drugs (p

Drug combinations can improve angiostatic cancer treatment efficacy and enable the reduction of side effects and drug resistance. Combining drugs is non-trivial due to the high number of possibilities. We applied a feedback system control (FSC) technique with a population-based stochastic search algorithm to navigate through the large parametric space of nine angiostatic drugs at four concentrations to identify optimal low-dose drug combinations. This implied an iterative approach of in vitro testing of endothelial cell viability and algorithm-based analysis. The optimal synergistic drug combination, containing erlotinib, BEZ-235 and RAPTA-C, was reached in a small number of iterations. Final drug combinations showed enhanced endothelial cell specificity and synergistically inhibited proliferation (p < 0.001), but not migration of endothelial cells, and forced enhanced numbers of endothelial cells to undergo apoptosis (p < 0.01). Successful translation of this drug combination was achieved in two preclinical in vivo tumor models. Tumor growth was inhibited synergistically and significantly (p < 0.05 and p < 0.01, respectively) using reduced drug doses as compared to optimal single-drug concentrations. At the applied conditions, single-drug monotherapies had no or negligible activity in these models. We suggest that FSC can be used for rapid identification of effective, reduced dose, multi-drug combinations for the treatment of cancer and other diseases.

The cell is a complex system involving numerous components, which may often interact in a non-linear dynamic manner. Diseases at the cellular level are thus likely to involve multiple cellular constituents and pathways. As some drugs, or drug combinations, may act synergistically on these multiple pathways, they might be more effective than the respective single target agents. Optimizing a drug mixture for a given disease in a particular patient is particularly challenging due to both the difficulty in the selection of the drug mixture components to start out with, and the all-important doses of these drugs to be applied. For n concentrations of m drugs, in principle, n(m) combinations will have to be tested. As this may lead to a costly and time-consuming investigation for each individual patient, we have developed a Feedback System Control (FSC) technique which can rapidly select the optimal drug-dose combination from the often millions of possible combinations. By testing this FSC technique in a number of experimental systems representing different disease states, we found that the response of cells to multiple drugs is well described by a low order, rather smooth, drug-mixture-input/drug-effect-output multidimensional surface. The main consequences of this are that optimal drug combinations can be found in a surprisingly small number of tests, and that translation from in vitro to in vivo is simplified. This points to the possibility of personalized optimal drug mixtures in the near future. This unexpectedly simple input-output relationship may also lead to a simple solution for handling the issue of human diversity in cancer therapeutics.

The chicken chorioallantoic membrane (CAM) is a simple, highly vascularized extraembryonic membrane, which performs multiple functions during embryonic development, including but not restricted to gas exchange. Over the last two decades, interest in the CAM as a robust experimental platform to study blood vessels has been shared by specialists working in bioengineering, development, morphology, biochemistry, transplant biology, cancer research and drug development. The tissue composition and accessibility of the CAM for experimental manipulation, makes it an attractive preclinical in vivo model for drug screening and/or for studies of vascular growth. In this article we provide a detailed review of the use of the CAM to study vascular biology and response of blood vessels to a variety of agonists. We also present distinct cultivation protocols discussing their advantages and limitations and provide a summarized update on the use of the CAM in vascular imaging, drug delivery, pharmacokinetics and toxicology.