Kinetic limitations of cooperativity based drug delivery systems

We study theoretically a novel drug delivery system that utilizes the overexpression of certain proteins in cancerous cells for cell specific chemotherapy. The system consists of dendrimers conjugated with "keys" (ex: folic acid) which "key-lock" bind to particular cell membrane proteins (ex: folate receptor). The increased concentration of "locks" on the surface leads to a longer residence time for the dendrimer and greater incorporation into the cell. Cooperative binding of the nanocomplexes leads to an enhancement of cell specificity. However, both our theory and detailed analysis of in-vitro experiments indicate that the degree of cooperativity is kinetically limited. We demonstrate that cooperativity and hence the specificity to particular cell type can be increased by making the strength of individual bonds weaker, and suggest a particular implementation of this idea. The implications of the work for optimizing the design of drug delivery vehicles are discussed.Comment: 4 pages, 4 figures, v3: minor revision

Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks

Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design

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

Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity

Tumour growth and drug resistance: an evolutionary view with perspectives in therapeutics

International audienceBACKGROUND Drug-induced drug resistance in cancer hasbeen attributed to diverse biological mechanisms at the individualcell or cell population scale, relying on stochastically or epigeneticallyvarying expression of phenotypes at the single cell level,and on the adaptability of tumours at the cell population level.SCOPE OF THIS 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 encompassthe great evolution that has designed multicellular organisms, aswell as smaller windows of evolution on the time scale of humandisease. We also present mathematical models used to predictdrug resistance in cancer and optimal control methods that cancircumvent it in combined therapeutic strategies.MAJOR CONCLUSIONS Plasticity in cancer cells, i.e., partialreversal to a stem-like status in individual cells and resultingadaptability of cancer cell populations, may be viewed as backwardevolution making cancer cell populations resistant to druginsult. This reversible plasticity is captured by mathematical modelsthat incorporate between-cell heterogeneity through continuousphenotypic variables. Such models have the benefit of beingcompatible with optimal control methods for the design of optimisedtherapeutic protocols involving combinations of cytotoxicand cytostatic treatments with epigenetic drugs and immunotherapies.GENERAL SIGNIFICANCE Gathering knowledge from cancerand evolutionary biology with physiologically based mathematicalmodels of cell population dynamics should provide oncologistswith a rationale to design optimised therapeutic strategiesto circumvent drug resistance, that still remains a major pitfall ofcancer therapeutics

Medium-Term Culture of Primary Oral Squamous Cell Carcinoma in a Three-Dimensional Model: Effects on Cell Survival Following Topical 5-Fluororacile Delivery by Drug-Loaded Matrix Tablets

Since the activity of several conventional anticancer drugs is restricted by resistance mechanisms and dose-limiting side-effects, the design of formulations for local application on malignant lesions seems to be an efficient and promising drug delivery approach. In this study, the effect of locally applied 5-FU on cell death was evaluated both in a SCC4/HEK001 model and in a newly proposed 3D outgrowth model of oral squamous cell carcinoma (OSCC). Initially, the optimal drug dose was established by delivery of solutions containing different amounts of 5-FU. The solution containing 1% (w/v) of 5-FU resulted effective in inducing cell death with complete eradication of cell colonies. Buccal tablets were designed to deliver 5-FU locoregionally to the cancer lesions of the oral cavity. Tablets were prepared using a drug loaded matrix of acrylic/methacrylic acid copolymer containing 1% (w/w) of 5-FU and applied on 3D outgrowths. The drug release from tablets appeared to be sufficient to induce cell death as confirmed by transmission electron microscopy and enzymatic assay (TUNEL). After 120 h of treatment, when about 90% of the drug had been discharged from the tablets into the culture environment, 5-FU caused loss of cell-cell communications and apoptotic cell death. After 192 h, a complete disaggregation of the 3D oral outgrowths and the death of all the cells was observed. Buccal matrix tablets could be considered a promising new approach to the locoregional treatment of OSCC. Risks of systemic toxicity are avoided since very low drug doses are delivered

Integrating Nanomembrane Separation with Plasmonic Detection for Real-Time Cell Culture Monitoring

To further understand cellular responses to drug treatment the dynamics of a reduced secretome shall be investigated. Currently there is no method for the detection of secreted small molecules in real time, label-free and with a high resolution. We present a novel design, which integrates nanopore filtration technology with highly sensitive plasmonic detection that allows real time monitoring of filtered molecules with a high spatial resolution and label free. The cell culture chamber is separated from the site of detection only by our biocompatible nanomembrane filter with a thickness of less than 100 nm to exclude the majority of background signals from the cell culture. The fast filtration of the cell culture constituents through the nanomembrane to the detector allows the observation of the dynamics of secreted molecules during cell culture and/or drug application. The setup offers new possibilities for drug screening and cell assays and may reveal new insights into cell signaling and drug responses. This setup shall be used to monitor cell culture or tissue culture without the necessity of labeling. This can be particularly important for the very popular “organ-on-a-chip” or “patient-on-a-chip” approaches to monitor tissue reactions to drug treatments with a high spatial resolution. Please click Additional Files below to see the full abstract