New design of nucleotide excision repair (NER) inhibitors for combination cancer therapy

Many cancer chemotherapy agents act by targeting the DNA of cancer cells, causing substantial damage within their genome and causing them to undergo apoptosis. An effective DNA repair pathway in cancer cells can act in a reverse way by removing these drug-induced DNA lesions, allowing cancer cells to survive, grow and proliferate. In this context, DNA repair inhibitors opened a new avenue in cancer treatment, by blocking the DNA repair mechanisms from removing the chemotherapy-mediated DNA damage. In particular, the nucleotide excision repair (NER) involves more than thirty protein–protein interactions and removes DNA adducts caused by platinum-based chemotherapy. The excision repair cross-complementation group 1 (ERCC1)-xeroderma pigmentosum, complementation group A (XPA) protein (XPA–ERCC1) complex seems to be one of the most promising targets in this pathway. ERCC1 is over expressed in cancer cells and the only known cellular function so far for XPA is to recruit ERCC1 to the damaged point. Here, we build upon our recent advances in identifying inhibitors for this interaction and continue our efforts to rationally design more effective and potent regulators for the NER pathway. We employed in silico drug design techniques to: (1) identify compounds similar to the recently discovered inhibitors, but more effective at inhibiting the XPA–ERCC1 interactions, and (2) identify different scaffolds to develop novel lead compounds. Two known inhibitor structures have been used as starting points for two ligand/structure-hybrid virtual screening approaches. The findings described here form a milestone in discovering novel inhibitors for the NER pathway aiming at improving the efficacy of current platinum-based therapy, by modulating the XPA–ERCC1 interaction.     

Compartmental modelling of the pharmacokinetics of a breast cancer resistance protein

A mathematical model for the pharmacokinetics of Hoechst 33342 following administration into a culture medium containing a population of transfected cells (HEK293 hBCRP) with a potent breast cancer resistance protein inhibitor, Fumitremorgin C (FTC), present is described. FTC is reported to almost completely annul resistance mediated by BCRP in vitro. This non-linear compartmental model has seven macroscopic sub-units, with 14 rate parameters. It describes the relationship between the concentration of Hoechst 33342 and FTC, initially spiked in the medium, and the observed change in fluorescence due to Hoechst 33342 binding to DNA. Structural identifiability analysis has been performed using two methods, one based on the similarity transformation/exhaustive modelling approach and the other based on the differential algebra approach. The analyses demonstrated that all models derived are uniquely identifiable for the experiments/observations available. A kinetic modelling software package, namely FACSIMILE (MPCA Software, UK), was used for parameter fitting and to obtain numerical solutions for the system equations. Model fits gave very good agreement with in vitro data provided by AstraZeneca across a variety of experimental scenarios.     

A comparative proteomic study to characterize the vinblastine resistance in human ovarian cancer cells

Drug resistance is a major impediment to the successful treatment of human cancers, including ovarian cancer. Vinblastine (VLB), an antimicrotubule agent, is one of the chemotherapeutic drugs that exhibit resistance in ovarian cancer patients. To determine the protein factors that are involved in vinblastine resistance in human ovarian cancer cells, a combination of sample pre-fractionation and high-resolution 2-DE proteomic analysis was performed. Approximately 1200 proteins were detected and quantitatively compared in both nuclear/membrane and cytosolic fractions. Sixty-nine proteins from the nuclear/membrane fraction showed altered expression levels, whereas 59 were altered in the cytosolic fraction between SKOV3 (vinblastine-sensitive) and SKVLB (vinblastine-resistant) cell lines. These proteins include membrane-associated, chromatin remodeling, cytoskeletal, and microtubule-associated proteins as well as others that regulate signal transduction. This study not only demonstrates a novel understanding of the mechanism of drug resistance but also provides a valuable resource for future studies on drug resistance to vinblastine. In addition, it also represents a good example of how to increase the protein dynamic range and reduce sample complexity using currently available tools.     

Biomarker discovery by proteomics‐based approaches for early detection and personalized medicine in colorectal cancer

About one million people per year develop colorectal cancer (CRC) and approximately half of them die. The extent of the disease (i.e. local invasion at the time of diagnosis) is a key prognostic factor. The 5‐year survival rate is almost 90% in the case of delimited CRC and 10% in the case of metastasized CRC. Hence, one of the great challenges in the battle against CRC is to improve early diagnosis strategies. Large‐scale proteomic approaches are widely used in cancer research to search for novel biomarkers. Such biomarkers can help in improving the accuracy of the diagnosis and in the optimization of personalized therapy. Herein, we provide an overview of studies published in the last 5 years on CRC that led to the identification of protein biomarkers suitable for clinical application by using proteomic approaches. We discussed these findings according to biomarker application, including also the role of protein phosphorylation and cancer stem cells in biomarker discovery. Our review provides a cross section of scientific approaches and can furnish suggestions for future experimental strategies to be used as reference by scientists, clinicians and researchers interested in proteomics for biomarker discovery.     

Reverse-phase protein arrays for application-orientated cancer research

A detailed and quantitative analysis of disease-relevant signaling will greatly contribute to our understanding of tumorigenesis and cancer progression, and thus open new strategies for drug discovery. However, throughput and sensitivity of currently established methods available for proteome profiling do not comply with the needs of clinical research such as high sample capacity and low sample consumption. Protein microarrays emerged as a promising alternative to analyze the abundance of proteins and their phosphorylation status on a high-throughput level. Here we summarize recent methodological advancements in the field of reverse-phase protein arrays and demonstrate their potential for clinical research as well as for in vitro applications.     

Proteomic and other analyses to determine the functional consequences of deregulated kallikrein-related peptidase (KLK) expression in prostate and ovarian cancer

Rapidly developing proteomic tools are improving detection of deregulated kallikrein-related peptidase (KLK) expression, at the protein level, in prostate and ovarian cancer, as well as facilitating the determination of functional consequences downstream. MS-driven proteomics uniquely allows for the detection, identification, and quantification of thousands of proteins in a complex protein pool, and this has served to identify certain KLKs as biomarkers for these diseases. In this review, we describe applications of this technology in KLK biomarker discovery and elucidate MS-based techniques that have been used for unbiased, global screening of KLK substrates within complex protein pools. Although MS-based KLK degradomic studies are limited to date, they helped to discover an array of novel KLK substrates. Substrates identified by MS-based degradomics are reported with improved confidence over those determined by incubating a purified or recombinant substrate and protease of interest, in vitro. We propose that these novel proteomic approaches represent the way forward for KLK research, in order to correlate proteolysis of biological substrates with tissue-related consequences, toward clinical targeting of KLK expression and function for cancer diagnosis, prognosis, and therapies.     

Proteomic approaches for discovery of new targets for vaccine and therapeutics against visceral leishmaniasis

Visceral leishmaniasis (VL) is the most devastating type caused by Leishmania donovani, Leishmania infantum, and Leishmania chagasi. The therapeutic mainstay is still based on the antiquated pentavalent antimonial against which resistance is now increasing. Unfortunately, due to the digenetic life cycle of parasite, there is significant antigenic diversity. There is an urgent need to develop novel drug/vaccine targets against VL for which the primary goal should be to identify and characterize the structural and functional proteins. Proteomics, being widely employed in the study of Leishmania seems to be a suitable strategy as the availability of annotated sequenced genome of Leishmania major has opened the door for dissection of both protein expression/regulation and function. Advances in clinical proteomic technologies have enable to enhance our mechanistic understanding of virulence/pathogenicity/host-pathogen interactions, drug resistance thereby defining novel therapeutic/vaccine targets. Expression proteomics exploits the differential expression of leishmanial proteins as biomarkers for application towards early diagnosis. Further using immunoproteomics efforts were also focused on evaluating responses to define parasite T-cell epitopes as vaccine/diagnostic targets. This review has highlighted some of the relevant developments in the rapidly emerging field of leishmanial proteomics and focus on its future applications in drug and vaccine discovery against VL.     

Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model

Use of the PharmaCloud can improve the quality of healthcare, but improvements are likely to be thwarted if physicians resist using the system. This study uses the dual-factor model to explain physicians’ resistance behaviors to system usage. The results of a field survey conducted in Taiwan showed that physicians’ resistance to using the PharmaCloud stemmed from regret avoidance, inertia, perceived value, and perceived threat. These results also indicate that system, information, and service qualities are the key determinants of the behavioral intention to use. This research advances the theoretical understanding of user acceptance and resistance to technology post-implementation and offers practical implications.     

Optimal robust control of drug delivery in cancer chemotherapy: A comparison between three control approaches

During the drug delivery process in chemotherapy, both of the cancer cells and normal healthy cells may be killed. In this paper, three mathematical cell-kill models including log-kill hypothesis, Norton–Simon hypothesis and E_max hypothesis are considered. Three control approaches including optimal linear regulation, nonlinear optimal control based on variation of extremals and H_∞-robust control based on μ-synthesis are developed. An appropriate cost function is defined such that the amount of required drug is minimized while the tumor volume is reduced. For the first time, performance of the system is investigated and compared for three control strategies; applied on three nonlinear models of the process. In additions, their efficiency is compared in the presence of model parametric uncertainties. It is observed that in the presence of model uncertainties, controller designed based on variation of extremals is more efficient than the linear regulation controller. However, H_∞-robust control is more efficient in improving robust performance of the uncertain models with faster tumor reduction and minimum drug usage.     

Systematic investigation of biomolecular interactions using combined frequency and motional resistance measurements

The resonance frequency of acoustic biosensors is today used as a label-free technique for detecting mass changes on sensor surfaces. In combination with an appropriate continuous flow system it has earlier been used for affinity and kinetic rate determination. Here, we assess the potential of a modified acoustic biosensor, monitoring also the real-time dissipation through the resistance of the sensor, to obtain additional kinetic information related to the structure and conformation of the molecules on the surface. Actual interaction studies, including an attempt to determine avidity, are presented along with thorough verification of the experimental setup utilizing true viscous load exposure together with protein and DNA immobilizations.True viscous loads show a linear relationship between resistance and frequency as expected. However, in the interaction studies between antibodies and proteins, as well as in the immobilization of DNA and proteins, higher surface concentrations of interacting molecules led to a decrease (i.e. deviation from the linear trend) in the differential resistance to frequency ratio. This is interpreted as increased surface rigidity at higher surface concentrations of immobilized molecules. Consequently, studies that aim at obtaining biological binding information, such as avidity, from real-time resistance and dissipation data should be conducted at low surface concentrations. In addition, the differential resistance to frequency relationship was found to be highly dependent on the rigidity of the preceding layer(s) of immobilized molecules. This dependence can be utilized to obtain a higher signal-to-noise ratio for resistance measurement by using low surface densities of immobilized interaction partners.     

Kinetic modelling of the role of the aldehyde dehydrogenase enzyme and the breast cancer resistance protein in drug resistance and transport

A compartmental model for the in vitro uptake kinetics of the anti-cancer agent topotecan (TPT) has been extended from a previously published model. The extended model describes the drug activity and delivery of the pharmacologically active form to the DNA target as well as the catalysis of the aldehyde dehydrogenase (ALDH) enzyme and the elimination of drug from the cytoplasm via the efflux pump. Verification of the proposed model is achieved using scanning-laser microscopy data from live human breast cancer cells. Before estimating the unknown model parameters from the experimental in vitro data it is essential to determine parameter uniqueness (or otherwise) from this imposed output structure. This is formally performed as a structural identifiability analysis, which demonstrates that all of the unknown model parameters are uniquely determined by the output structure corresponding to the experiment.     

Proteomics in human cancer research

Proteomics is now widely employed in the study of cancer. Many laboratories are applying the rapidly emerging technologies to elucidate the underlying mechanisms associated with cancer development, progression, and severity in addition to developing drugs and identifying patients who will benefit most from molecular targeted compounds. Various proteomic approaches are now available for protein separation and identification, and for characterization of the function and structure of candidate proteins. In spite of significant challenges that still exist, proteomics has rapidly expanded to include the discovery of novel biomarkers for early detection, diagnosis and prognostication (clinical application), and for the identification of novel drug targets (pharmaceutical application). To achieve these goals, several innovative technologies including 2-D-difference gel electrophoresis, SELDI, multidimensional protein identification technology, isotope-coded affinity tag, solid-state and suspension protein array technologies, X-ray crystallography, NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulation have evolved, and are being used in different combinations. This review provides an overview of the field of proteomics and discusses the key proteomic technologies available to researchers. It also describes some of the important challenges and highlights the current pharmaceutical and clinical applications of proteomics in human cancer research.