Pharmacodynamic drug-drug interactions (DDIs) occur when the pharmacological effect of one drug is altered by that of another drug in a combination regimen. DDIs often are classified as synergistic, additive, or antagonistic in nature, albeit these terms are frequently misused. Within a complex pathophysiological system, the mechanism of interaction may occur at the same target or through alternate pathways. Quantitative evaluation of pharmacodynamic DDIs by employing modeling and simulation approaches is needed to identify and optimize safe and effective combination therapy regimens. This review investigates the opportunities and challenges in pharmacodynamic DDI studies and highlights examples of quantitative methods for evaluating pharmacodynamic DDIs, with a particular emphasis on the use of mechanism-based modeling and simulation in DDI studies. Advancements in both experimental and computational techniques will enable the application of better, modelinformed assessments of pharmacodynamic DDIs in drug discovery, development, and therapeutics.

ADH1B/ALDH2 genotypes affect the risk of esophageal cancer, and the risk is modified by alcohol consumption, ethnicity, and gender.

EC endoscopic screening is cost-beneficial in high-risk areas of China. Policy-makers should consider the cost-benefit, population acceptance, and local economic status when choosing suitable screening strategies.

The incidence patterns of HPV-associated oropharyngeal cancer and head and neck cancer overall show contrasting trends. Findings highlight the need to surveil HPV-associated oropharyngeal cancer separately from other cancers of the head and neck region in order to monitor these emerging trends.

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

To survive, cells need to avoid excessive volume change that jeopardizes structural integrity and stability of the intracellular milieu. Searching for the molecular identity of volume-regulated anion channel (VRAC) has yielded multiple potential candidates, but none has been confirmed. Recently, it is reported that leucine-rich repeat-containing 8A (LRRC8A) is a main molecular determinant of VRAC current. The biological functions of LRRC8 family proteins are poorly understood, particularly in cancer. In the present study, we investigated LRRC8A in the most common cancers of the digestive system. LRRC8A proteins were found to be abundantly expressed in the esophagus, stomach, duodenum, colon, rectum, liver and pancreas. LRRC8A was elevated in 60% of colorectal cancer patient tissues, which was higher than that in patients with cancer of the esophagus, stomach, duodenum, liver and pancreas. Colon cancer patients with high- expressed LRRC8A had a survival time of 54.9±5.5 months, shorter than that of patients with low-expressed LRRC8A (77.1±3.7). Moreover, survival time (52.6±7.3 months) of patients with metastases in the lymph nodes was shorter than that of patients without positive lymph nodes (72.2±3.6); patients with positive lymph nodes and an elevated LRRC8A expression had the highest mortality rate (~80%). These rates were not observed in rectal cancer. After LRRC8A protein was knocked down in colon cancer HCT116 cells, VRAC currents, migration and tumorigenesis in nude mice were significantly inhibited. In conclusion, we propose that LRRC8A could be a novel prognostic biomarker for colon cancer patient survival, and that the elevated expression of LRRC8A may enhance cancer cell growth and metastasis, and worsen the outcome of patients.

SummaryDespite decades of effort, pancreatic adenocarcinoma (PDAC) remains an intractable clinical challenge.An insufficient understanding of mechanisms underlying tumor cell responses to chemotherapy contributes significantly to the lack of effective treatment regimens. Here, paclitaxel, a first-line chemotherapeutic agent, was observed to interact synergistically with birinapant, a Second Mitochondrial-derived Activator of Caspases mimetic. Therefore, we investigated molecular-level drug interaction mechanisms using comprehensive, reproducible, and well-controlled ion-current-based MS1 quantification (IonStar). By analyzing 40 biological samples in a single batch, we compared temporal proteomic responses of PDAC cells treated with birinapant and paclitaxel, alone and combined. Using stringent criteria (e.g. strict false-discovery-rate FDR control, 2 peptides/protein), we quantified 4069 unique proteins confidently (99.8% without any missing data), and 541 proteins were significantly altered in the three treatment groups, with a FDR of <1%. Interestingly, most of these proteins were altered only by combined birinapant/paclitaxel, and these predominantly represented three biological processes: mitochondrial function, cell growth and apoptosis, and cell cycle arrest. Proteins responsible for activation of oxidative phosphorylation, fatty acid β-oxidation, and inactivation of aerobic glycolysis were altered largely by combined birinapant/paclitaxel compared to single drugs, suggesting the Warburg effect, which is critical for survival and proliferation of cancer cells, was alleviated by the combination treatment. Metabolic profiling was performed to confirm substantially greater suppression of the Warburg effect by the combined agents compared to either drug alone. Immunoassays confirmed proteomic data revealing changes in apoptosis/survival signaling pathways, such as inhibition of PI3K/AKT, JAK/STAT, and MAPK/ERK signal transduction, as well as induction of G2/M arrest, and showed the drug combination induced much more apoptosis than did single agents. Overall, this in-depth, large-scale proteomics study provided novel insights into molecular mechanisms underlying synergy of combined birinapant/paclitaxel, and describes a proteomics/informatics pipeline that can be applied broadly to the development of cancer drug combination regimens.4

The possible cross reactions indicated by solid‐state NMR between cyanate functionalized resin and epoxy functionalized resin have been investigated by using both natural abundance and labeled monofunctional model compounds. These soluble products were isolated and purified by silica gel adsorption chromatography and gel permeation chromatography. They were fully characterized by high resolution 1H‐, 13C‐, 15N‐NMR spectroscopy and by mass spectrometry. The major cross‐reaction product is a racemic mixture of enantiomers, which contain an oxazolidinone ring formed by one cyanate molecule and two epoxy molecules. However, epoxy consumption lags cyanate consumption in the overall reaction as triazine formation from the cyanate is much faster than the two competing reactions, the cross reaction between cyanate and epoxy, and the self‐polymerization of epoxy, under the conditions investigated. The cross reaction between cyanate and epoxy is limited. Approximately 12% of cross reaction between cyanate and epoxy was found in the overall reaction. In addition to the cross reactions of epoxy and cyanate, the reactions of epoxy and the carbamate, which is the major side product for the curing reaction of cyanate resin in solution, have also been investigated, and the mechanism of these reactions discussed. From the reactions of epoxy and carbamate, several products related to cross reaction between epoxy and cyanate have been isolated and identified. It is suggested that the reaction of epoxy and carbamate is one of the pathways in the overall cross reaction between epoxy and cranate resins. Finally, the mechanism of the overall cross‐curing reaction between the diepoxy and dicyanate mixed resins is discussed. © 1994 John Wiley & Sons, Inc.