FEATURED CANCER-PHYSICS TRILOGY
Dr. J. James Frost highlights the discussion of intelligence as applied to cancer; computation and its limits and develops a new approach to advanced personalized oncology where elucidation of the patient's intrinsic cancer computational machine and its gameplay strategies, coupled to recent developments in AI human gameplay, including bluffing and deception, can lead to vastly improved strategies for the oncologist to defeat the patient's cancer.
"Is there a parallel to be drawn between bluffing in poker and bluffing in cancer’s game with the oncologist?"
Symmetry and symmetry breaking principles are applied to cancer, categorizing them into combinatorial, geometric, and functional aspects, exploring their relevance to features like EMT, tumor heterogeneity, and functional interaction networks, offering a novel perspective that may contribute to understanding cancer's origin, spread, treatment, and resistance.
Cancer is viewed as an intelligent system with collaborative and computing cells, highlighting the limitations of current research and treatment, advocating for a new paradigm, and exploring the application of computational concepts to describe cancer's intelligent features, leading to implications for future research.
I wrote the lead chapter, "What Cancer Is" exploring novel therapeutic insights and a deeper understanding of cancer's fundamental nature for the book, Cancer, Complexity, Computation, which offers a diverse compendium of interdisciplinary perspectives on cancer from top experts in oncology, complexity theory, mathematics, and computer science.
SELECTED PUBLICATIONS
Dr. Frost's diverse research portfolio also covers topics ranging from opioid antagonism in humans to quantitative imaging in clinical trials, symmetry in cancer, brain amyloid imaging, and novel PET sensors for apoptosis detection, reflecting a comprehensive approach to understanding and addressing complex biomedical issues.
The study analyzes opioid receptor blockade in non-human animal and human studies, providing precise estimates for central mu-opioid receptor (MOR) occupancy following antagonist administration (naloxone and naltrexone), suggesting common doses are sufficient for full MOR blockade, and introducing web applications for experiment planning and evaluation.
I wrote the lead chapter, "What Cancer Is" exploring novel therapeutic insights and a deeper understanding of cancer's fundamental nature.
The book, Cancer, Complexity, Computation, offers a diverse compendium of interdisciplinary perspectives on cancer, exploring its origins, developmental complexities, treatment approaches using artificial intelligence, and its role in evolution, featuring contributions from top experts in oncology, complexity theory, mathematics, and computer science.
Cancer is viewed as an intelligent system with collaborative and computing cells, highlighting the limitations of current research and treatment, advocating for a new paradigm, and exploring the application of computational concepts to describe cancer's intelligent features, leading to implications for future research.
The pharmaceutical industry, investing significantly in R&D with a focus on developing new drugs for novel targets, faces challenges in clinical success rates and high costs; quantitative modeling and reliable imaging technologies are explored as essential tools to enhance decision-making in drug development, emphasizing the need for effective analytic methods in evaluating and measuring success at various stages of clinical trials.
Symmetry and symmetry breaking principles are applied to cancer, categorizing them into combinatorial, geometric, and functional aspects, exploring their relevance to features like EMT, tumor heterogeneity, and functional interaction networks, offering a novel perspective that may contribute to understanding cancer's origin, spread, treatment, and resistance.
Advancements in molecular imaging, particularly in brain amyloid and inflammation imaging, contribute to understanding disease mechanisms, identifying treatment targets, and discovering diagnostic biomarkers, with a focus on early intervention for amyloid reduction in pre-dementia patients and increased attention to inflammation in Alzheimer's disease.
Translational imaging aims to predict future blockbuster drugs and customize approved drugs for individual patients using diagnostic biomarker imaging, but the emergence of 'niche busters' is anticipated as molecular diversity in human diseases is better understood, emphasizing the role of molecular imaging in identifying relevant drug targets across therapeutic areas; challenges in drug development, especially for novel targets, highlight the need for careful evaluation of preclinical imaging limitations, quantitative modeling, and efficient transitioning of candidates to manage costs and success rates.
The study investigates regional brain mu-opioid receptor (MOR) and delta-opioid receptor (DOR) availability in recently abstinent alcohol-dependent individuals compared to healthy controls, finding significantly higher [11C]carfentanil (CFN) binding potential in multiple brain regions for alcohol-dependent subjects, suggesting MOR up-regulation or reduced endogenous opioid peptides due to long-term alcohol consumption or withdrawal, highlighting the MOR's prominent role in alcohol dependence.
The study demonstrates the potential of the novel PET voltage sensor 18F-fluorobenzyl triphenyl phosphonium (18F-FBnTP) in quantifying the time-dependent apoptotic effects of taxanes paclitaxel and docetaxel in vitro and in vivo, showing a linear decline in 18F-FBnTP cellular uptake with paclitaxel treatment duration, correlating with apoptotic markers, and indicating its utility for early detection and quantitative assessment of apoptotic drug actions using PET.
CNS drug development faces a high failure rate, but in vivo imaging, particularly using target biomarkers, provides a promising approach to derisk drug molecules at various stages, with the potential for guiding personalized medicine by understanding disease mechanisms and influencing drug development choices and dosages through collaborative efforts between academia and the pharmaceutical industry.
Elevated mu-opioid receptor (mOR) binding in frontal and temporal cortical regions, measured via positron emission tomography with [11C]carfentanil, is identified as a significant independent predictor of the time to relapse to cocaine use in non-treatment-seeking adult users, emphasizing the crucial role of the brain's endogenous opioid system in cocaine addiction.
The study utilizes PET imaging to measure the kinetics and distribution of the selective delta-opioid antagonist 11C-methylnaltrindole (11C-MeNTI) and the mu-opioid agonist 11C-carfentanil (11C-CFN) in patients with various lung carcinomas, detecting increased binding in all tumor types and suggesting the suitability of these probes for investigating lung carcinoma biology.