Preclinical PK/PD model for the combinatorial use of dexamethasone and sulpiride in the treatment of breast cancer

Computationally repurposing drugs for breast cancer subtypes using a network-based approach

‘De novo’ drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called ‘in silico’ drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.

Advantages and drawbacks of dexamethasone in glioblastoma multiforme

The most widespread, malignant, and deadliest type of glial tumor is glioblastoma multiforme (GBM). Despite radiation, chemotherapy, and radical surgery, the median survival of afflicted individuals is about 12 months. Unfortunately, existing therapeutic interventions are abysmal. Dexamethasone (Dex), a synthetic glucocorticoid, has been used for many years to treat brain edema and inflammation caused by GBM. Several investigations have recently shown that Dex also exerts antitumoral effects against GBM. On the other hand, more recent disputed findings have questioned the long-held dogma of Dex treatment for GBM. Unfortunately, steroids are associated with various undesirable side effects, including severe immunosuppression and metabolic changes like hyperglycemia, which may impair the survival of GBM patients. Current ideas and concerns about Dex's effects on GBM cerebral edema, cell proliferation, migration, and its clinical outcomes were investigated in this study.

Advancement of Sensor Integrated Organ-on-Chip Devices

Organ-on-chip devices have provided the pharmaceutical and tissue engineering worlds much hope since they arrived and began to grow in sophistication. However, limitations for their applicability were soon realized as they lacked real-time monitoring and sensing capabilities. The users of these devices relied solely on endpoint analysis for the results of their tests, which created a chasm in the understanding of life between the lab the natural world. However, this gap is being bridged with sensors that are integrated into organ-on-chip devices. This review goes in-depth on different sensing methods, giving examples for various research on mechanical, electrical resistance, and bead-based sensors, and the prospects of each. Furthermore, the review covers works conducted that use specific sensors for oxygen, and various metabolites to characterize cellular behavior and response in real-time. Together, the outline of these works gives a thorough analysis of the design methodology and sophistication of the current sensor integrated organ-on-chips.

Pharmacokinetic/Pharmacodynamic Modeling of the Anti-Cancer Effect of Dexamethasone in Pancreatic Cancer Xenografts and Anticipation of Human Efficacious Doses

Dexamethasone (DEX), a synthetic glucocorticoid, exhibited anti-cancer efficacy in pancreatic xenografts derived from patient tumor tissue or cancer cell lines. The aim of this study was to establish pharmacokinetic/pharmacodynamic (PK/PD) models to quantitatively characterize the inhibitory effect of DEX on tumor growth as well as its discrepancy among 3 xenograft models. Data of tumor growth profiles were collected from a patient-derived xenograft (PDX) model in NOD/SCID mice and 2 cell line-derived (PANC-1 and SW1990) xenograft models in BALB/c nude mice. Empirical PK/PD models were developed to establish mathematical relationships between plasma concentration of DEX and tumor growth dynamics after integrating PK parameters extracted from literature. Drug effect in each model was well described by a linear inhibitory function with a potency factor of 4.67, 3.14, and 2.35 L/mg for PDX, PANC-1, and SW1990 xenograft, respectively. Human efficacious doses of DEX were preliminarily predicted through model-based simulation, and 60% tumor growth inhibition at clinical exposure corresponded to a daily dose range of 26-52 mg intravenously. This modeling work quantified the preclinical anti-cancer effect of DEX and demonstrated the feasibility of its medication in pancreatic cancer, which would be conductive to future translational research.