Toward An Ethics of Reciprocity: Ethnobotanical Knowledge And Medicinal Plants as Cancer Therapies

This article develops a reciprocity ethics of the environment through a discussion of ethnobotanical medicines used in the treatment of cancer. The moral virtue of reciprocity, defined as the returning of good when good is received or anticipated, is central to the posthumanist rethinking of human relationships to the plant world. As herbal medicines are used progressively more around the globe and as plant diversity decreases as a result of habitat loss and climate change, an ethics of reciprocity should be a concern for environmental philosophers and conservationists. Aldo Leopold’s land ethic and J. Baird Callicott’s distinction between deontological and prudential environmental ethics provide theoretical contexts for the development of a reciprocity ethics vis-à-vis ethnobotanical species. While this article does not necessarily specify modes or forms of reciprocity, it does outline some of the more prominent ethnobotanical species used in the treatment of cancer, including those from Native American, African, Chinese, and Indian traditions. In the form of a dialogue between the fields of ethnobotany, herbal medicine, and environmental philosophy, this article presents a position from which further articulations of reciprocity can be developed, particularly those involving the rights of indigenous cultures and plants

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies

Decitabine potentiates efficacy of doxorubicin in a preclinical trastuzumab-resistant HER2-positive breast cancer models

Acquired drug resistance and metastasis in breast cancer (BC) are coupled with epigenetic deregulation of gene expression. Epigenetic drugs, aiming to reverse these aberrant transcriptional patterns and sensitize cancer cells to other therapies, provide a new treatment strategy for drug-resistant tumors. Here we investigated the ability of DNA methyltransferase (DNMT) inhibitor decitabine (DAC) to increase the sensitivity of BC cells to anthracycline antibiotic doxorubicin (DOX). Three cell lines representing different molecular BC subtypes, JIMT-1, MDA-MB-231 and T-47D, were used to evaluate the synergy of sequential DAC + DOX treatment in vitro. The cytotoxicity, genotoxicity, apoptosis, and migration capacity were tested in 2D and 3D cultures. Moreover, genome-wide DNA methylation and transcriptomic analyses were employed to understand the differences underlying DAC responsiveness. The ability of DAC to sensitize trastuzumab-resistant HER2-positive JIMT-1 cells to DOX was examined in vivo in an orthotopic xenograft mouse model. DAC and DOX synergistic effect was identified in all tested cell lines, with JIMT-1 cells being most sensitive to DAC. Based on the whole-genome data, we assume that the aggressive behavior of JIMT-1 cells can be related to the enrichment of epithelial-to-mesenchymal transition and stemness-associated pathways in this cell line. The four-week DAC + DOX sequential administration significantly reduced the tumor growth, DNMT1 expression, and global DNA methylation in xenograft tissues. The efficacy of combination therapy was comparable to effect of pegylated liposomal DOX, used exclusively for the treatment of metastatic BC. This work demonstrates the potential of epigenetic drugs to modulate cancer cells' sensitivity to other forms of anticancer therapy.publishedVersio

Endoscopic Ultrasound in Pancreatic Cancer

Endoscopic ultrasound (EUS) has been developed over the course of the last 50 years. This technique has been shown to improve diagnosis, provide more accurate local information with regards to staging and enhance prediction of surgical resectability. Further to this, minimally-invasive local techniques have been developed, and continue to be developed, to provide both active and palliative management within the treatment schema for pancreatic cancer (PC)

A Kernelisation Approach for Multiple d-Hitting Set and Its Application in Optimal Multi-Drug Therapeutic Combinations

Therapies consisting of a combination of agents are an attractive proposition, especially in the context of diseases such as cancer, which can manifest with a variety of tumor types in a single case. However uncovering usable drug combinations is expensive both financially and temporally. By employing computational methods to identify candidate combinations with a greater likelihood of success we can avoid these problems, even when the amount of data is prohibitively large. Hitting Set is a combinatorial problem that has useful application across many fields, however as it is NP-complete it is traditionally considered hard to solve exactly. We introduce a more general version of the problem (α,β,d)-Hitting Set, which allows more precise control over how and what the hitting set targets. Employing the framework of Parameterized Complexity we show that despite being NP-complete, the (α,β,d)-Hitting Set problem is fixed-parameter tractable with a kernel of size O(αdkd) when we parameterize by the size k of the hitting set and the maximum number α of the minimum number of hits, and taking the maximum degree d of the target sets as a constant. We demonstrate the application of this problem to multiple drug selection for cancer therapy, showing the flexibility of the problem in tailoring such drug sets. The fixed-parameter tractability result indicates that for low values of the parameters the problem can be solved quickly using exact methods. We also demonstrate that the problem is indeed practical, with computation times on the order of 5 seconds, as compared to previous Hitting Set applications using the same dataset which exhibited times on the order of 1 day, even with relatively relaxed notions for what constitutes a low value for the parameters. Furthermore the existence of a kernelization for (α,β,d)-Hitting Set indicates that the problem is readily scalable to large datasets

The microfluidic technique and the manufacturing of polysaccharide nanoparticles

Themicrofluidic technique has emerged as a promising tool to accelerate the clinical translation of nanoparticles, and its application affects several aspects, such as the production of nanoparticles and the in vitro characterization in the microenvironment, mimicking in vivo conditions. This review covers the general aspects of the microfluidic technique and its application in several fields, such as the synthesis, recovering, and samples analysis of nanoparticles, and in vitro characterization and their in vivo application. Among these, advantages in the production of polymeric nanoparticles in a well-controlled, reproducible, and high-throughput manner have been highlighted, and detailed descriptions of microfluidic devices broadly used for the synthesis of polysaccharide nanoparticles have been provided. These nanoparticulate systems have drawn attention as drug delivery vehicles over many years; nevertheless, their synthesis using themicrofluidic technique is still largely unexplored. This review deals with the use of the microfluidic technique for the synthesis of polysaccharide nanoparticles; evaluating features of the most studied polysaccharide drug carriers, such as chitosan, hyaluronic acid, and alginate polymers. The critical assessment of the most recent research published in literature allows us to assume that microfluidics will play an important role in the discovery and clinical translation of nanoplatforms

Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation

Background Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level. Scope of review We focus on intra-tumour heterogeneity, namely between-cell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies. Major conclusions Plasticity in cancer cells, i.e., partial reversal to a stem-like status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies. General significance Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics. This article is part of a Special Issue entitled “System Genetics” Guest Editor: Dr. Yudong Cai and Dr. Tao Huang

Integrated Network Pharmacology Approach for Drug Combination Discovery : A Multi-Cancer Case Study

Simple Summary Current treatments for complex diseases, including cancer, are generally characterized by high toxicity due to their low selectivity for target cells. Moreover, patients often develop drug resistance, hence becoming less sensitive to the therapy. For this reason, novel, improved, and more specific pharmacological therapies are needed. The high cost and the time required to develop new drugs poses the attention on the development of computational methods for drug repositioning and combination therapy prediction. In this study, we developed an integrated network pharmacology framework that combines mechanistic and chemocentric approaches in order to predict potential drug combinations for cancer therapy. We applied our paradigm in five cancer types, which we used as case studies. Our strategy can be applied to the study of any complex disease by guiding the prioritization of drug combinations. Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.Peer reviewe