BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems

We developed a detailed, whole-body physiologically based pharmacokinetic (PBPK) modeling tool for calculating the distribution of pharmaceutical agents in the various tissues and organs of a human or animal as a function of time. Ordinary differential equations (ODEs) represent the circulation of body fluids through organs and tissues at the macroscopic level, and the biological transport mechanisms and biotransformations within cells and their organelles at the molecular scale. Each major organ in the body is modeled as composed of one or more tissues. Tissues are made up of cells and fluid spaces. The model accounts for the circulation of arterial and venous blood as well as lymph. Since its development was fueled by the need to accurately predict the pharmacokinetic properties of imaging agents, BioDMET is more complex than most PBPK models. The anatomical details of the model are important for the imaging simulation endpoints. Model complexity has also been crucial for quickly adapting the tool to different problems without the need to generate a new model for every problem. When simpler models are preferred, the non-critical compartments can be dynamically collapsed to reduce unnecessary complexity. BioDMET has been used for imaging feasibility calculations in oncology, neurology, cardiology, and diabetes. For this purpose, the time concentration data generated by the model is inputted into a physics-based image simulator to establish imageability criteria. These are then used to define agent and physiology property ranges required for successful imaging. BioDMET has lately been adapted to aid the development of antimicrobial therapeutics. Given a range of built-in features and its inherent flexibility to customization, the model can be used to study a variety of pharmacokinetic and pharmacodynamic problems such as the effects of inter-individual differences and disease-states on drug pharmacokinetics and pharmacodynamics, dosing optimization, and inter-species scaling. While developing a tool to aid imaging agent and drug development, we aimed at accelerating the acceptance and broad use of PBPK modeling by providing a free mechanistic PBPK software that is user friendly, easy to adapt to a wide range of problems even by non-programmers, provided with ready-to-use parameterized models and benchmarking data collected from the peer-reviewed literature

Prolonged survival in patients with breast cancer and a history of brain metastases: results of a preplanned subgroup analysis from the randomized phase III BEACON trial

Purpose: Conventional chemotherapy has limited activity in patients with breast cancer and brain metastases (BCBM). Etirinotecan pegol (EP), a novel long-acting topoisomerase-1 inhibitor, was designed using advanced polymer technology to preferentially accumulate in tumor tissue including brain metastases, providing sustained cytotoxic SN38 levels. Methods: The phase 3 BEACON trial enrolled 852 women with heavily pretreated locally recurrent or metastatic breast cancer between 2011 and 2013. BEACON compared EP with treatment of physician’s choice (TPC; eribulin, vinorelbine, gemcitabine, nab-paclitaxel, paclitaxel, ixabepilone, or docetaxel) in patients previously treated with anthracycline, taxane, and capecitabine, including those with treated, stable brain metastases. The primary endpoint, overall survival (OS), was assessed in a pre-defined subgroup of BCBM patients; an exploratory post hoc analysis adjusting for the diagnosis-specific graded prognostic assessment (GPA) index was also conducted. Results: In the trial, 67 BCBM patients were randomized (EP, n = 36; TPC, n = 31). Treatment subgroups were balanced for baseline characteristics and GPA indices. EP was associated with a significant reduction in the risk of death (HR 0.51; P < 0.01) versus TPC; median OS was 10.0 and 4.8 months, respectively. Improvement in OS was observed in both poorer and better GPA prognostic groups. Survival rates at 12 months were 44.4% for EP versus 19.4% for TPC. Consistent with the overall BEACON population, fewer patients on EP experienced grade ≥3 toxicity (50 vs. 70%). Conclusions: The significant improvement in survival in BCBM patients provides encouraging data for EP in this difficult-to-treat subgroup of patients. A phase three trial of EP in BCBM patients is underway (ClinicalTrials.gov NCT02915744)

Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology