A Physiologically-Based Pharmacokinetic Model of the Brain Considering Regional Lipid Variance.

Modeling and simulation of the central nervous system provides a tool for understanding and predicting the distribution of small molecules throughout the brain tissue and cerebral spinal fluid (CSF), and these efforts often rely on empirical data to make predictions of distributions to move toward a better mechanistic understanding. A physiologically based pharmacokinetic model presented here incorporates multiple means of drug distribution to assemble a model for understanding potential factors that may determine the distribution of drugs across various regions of the brain, including both intra- and extracellular regions. Two classes of parameters are presented. The first concerns regional gross anatomic variability of the brain; the second concerns estimation of unbound fractions of drugs using know membrane phospholipid heterogeneity derived from regional lipid content. The model was then tested by comparing its outcomes to data from published human pharmacokinetic studies of acetaminophen, morphine, and phenytoin. The alignment of model predictions in the plasma, CSF, and tissue concentrations with the published data from studies of those three drugs suggests that the model can be a template for identifying drug localization in the brain. Clearly, knowledge of differentiated drug distribution in the brain is a requisite step in postulating pharmacodynamic and certain disease mechanisms. SIGNIFICANCE STATEMENT: This study concerns the application of heterogenous lipid distribution in brain tissue to predict regional variations in drug distribution in the brain via a mathematical model, thus expanding upon the current understanding of mechanisms of drug distribution in the central nervous system.

Estimating parameters of nonlinear dynamic systems in pharmacology using chaos synchronization and grid search.

Bridging fundamental approaches to model optimization for pharmacometricians, systems pharmacologists and statisticians is a critical issue. These fields rely primarily on Maximum Likelihood and Extended Least Squares metrics with iterative estimation of parameters. Our research combines adaptive chaos synchronization and grid search to estimate physiological and pharmacological systems with emergent properties by exploring deterministic methods that are more appropriate and have potentially superior performance than classical numerical approaches, which minimize the sum of squares or maximize the likelihood. We illustrate these issues with an established model of cortisol in human with nonlinear dynamics. The model describes cortisol kinetics over time, including its chaotic oscillations, by a delay differential equation. We demonstrate that chaos synchronization helps to avoid the tendency of the gradient-based optimization algorithms to end up in a local minimum. The subsequent analysis illustrates that the hybrid adaptive chaos synchronization for estimation of linear parameters with coarse-to-fine grid search for optimal values of non-linear parameters can be applied iteratively to accurately estimate parameters and effectively track trajectories for a wide class of noisy chaotic systems.

Pharmacokinetics and pharmacodynamics of a single dose Nilotinib in individuals with Parkinson's disease.

Nilotinib is a broad-based tyrosine kinase inhibitor with the highest affinity to inhibit Abelson (c-Abl) and discoidin domain receptors (DDR1/2). Preclinical evidence indicates that Nilotinib reduces the level of brain alpha-synuclein and attenuates inflammation in models of Parkinson's disease (PD). We previously showed that Nilotinib penetrates the blood-brain barrier (BBB) and potentially improves clinical outcomes in individuals with PD and dementia with Lewy bodies (DLB). We performed a physiologically based population pharmacokinetic/pharmacodynamic (popPK/PD) study to determine the effects of Nilotinib in a cohort of 75 PD participants. Participants were randomized (1:1:1:1:1) into five groups (n = 15) and received open-label random single dose (RSD) 150:200:300:400 mg Nilotinib vs placebo. Plasma and cerebrospinal fluid (CSF) were collected at 1, 2, 3, and 4 hours after Nilotinib administration. The results show that Nilotinib enters the brain in a dose-independent manner and 200 mg Nilotinib increases the level of 3,4-Dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA), suggesting alteration to dopamine metabolism. Nilotinib significantly reduces plasma total alpha-synuclein and appears to reduce CSF oligomeric: total alpha-synuclein ratio. Furthermore, Nilotinib significantly increases the CSF level of triggering receptors on myeloid cells (TREM)-2, suggesting an anti-inflammatory effect. Taken together, 200 mg Nilotinib appears to be an optimal single dose that concurrently reduces inflammation and engages surrogate disease biomarkers, including dopamine metabolism and alpha-synuclein.

Chaos synchronization and Nelder-Mead search for parameter estimation in nonlinear pharmacological systems: Estimating tumor antigenicity in a model of immunotherapy.

In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. The predictive capability of pharmacological models depends heavily on the ability to track the system and to accurately determine parameters with reference to the sensitivity in projected outcomes. To closely track chaotic systems, one may choose to apply chaos synchronization. An advantageous byproduct of this methodology is the ability to quantify model parameters. In this paper, we illustrate the use of chaos synchronization combined with Nelder-Mead search to estimate parameters of the well-known Kirschner-Panetta model of IL-2 immunotherapy from noisy data. Chaos synchronization with Nelder-Mead search is shown to provide more accurate and reliable estimates than Nelder-Mead search based on an extended least squares (ELS) objective function. Our results underline the strength of this approach to parameter estimation and provide a broader framework of parameter identification for nonlinear models in pharmacology.

Pharmacokinetic modeling as an approach to assessing the safety of residual formaldehyde in infant vaccines.

Formaldehyde is a one-carbon, highly water-soluble aldehyde that is used in certain vaccines to inactivate viruses and to detoxify bacterial toxins. As part of the manufacturing process, some residual formaldehyde can remain behind in vaccines at levels less than or equal to 0.02%. Environmental and occupational exposure, principally by inhalation, is a continuing risk assessment focus for formaldehyde. However, exposure to formaldehyde via vaccine administration is qualitatively and quantitatively different from environmental or occupational settings and calls for a different perspective and approach to risk assessment. As part of a rigorous and ongoing process of evaluating the safety of biological products throughout their lifecycle at the FDA, we performed an assessment of formaldehyde in infant vaccines, in which estimates of the concentrations of formaldehyde in blood and total body water following exposure to formaldehyde-containing vaccines at a single medical visit were compared with endogenous background levels of formaldehyde in a model 2-month-old infant. Formaldehyde levels were estimated using a physiologically-based pharmacokinetic (PBPK) model of formaldehyde disposition following intramuscular (IM) injection. Model results indicated that following a single dose of 200 µg, formaldehyde is essentially completely removed from the site of injection within 30 min. Assuming metabolism at the site of injection only, peak concentrations of formaldehyde in blood/total body water were estimated to be 22 µg/L, which is equivalent to a body burden of 66 µg or <1% of the endogenous level of formaldehyde. Predicted levels in the lymphatics were even lower. Assuming no adverse effects from endogenous formaldehyde, which exists in blood and extravascular water at background concentrations of 0.1 mM, we conclude that residual, exogenously applied formaldehyde continues to be safe following incidental exposures from infant vaccines.

Commentary on ''Toxicity testing in the 21st century: a vision and a strategy''.

Toxicity Testing in the 21st Century: A Vision and a Strategy, from the National Research Council Committee on Toxicity Testing and Assessment of Environmental Agents, presents a vision wherein toxicology testing moves from feeding test substances to animals for their lifetimes, and assessing clinical laboratory and histopathological changes, to human tissue studies made suitable by recent technological advances in computational biology, toxicogenomics, and the like. This is to be accomplished by elucidating toxicity pathways complemented by targeted testing. The report focuses on the array of available new concepts and attendant technology that the committee considers relevant to its proffer, but, in the final analysis, it describes little in the way of robust strategy for achieving the stated goals. From that perspective, the vision, as described, is no more innovative or far-reaching than goals directed at the utility of cellular metabolism measurements put forth fifty years ago. The report generally lacks the coherence and organization that could have given greater credibility to the committee's deliberative effort.

Kinetic and dynamic models of diving gases in decompression sickness prevention.

Decompression sickness is a complex phenomenon involving gas exchange, bubble dynamics and tissue response. Relatively simple deterministic compartmental models using empirically derived parameters have been the mainstay of the practice for preventing decompression sickness since the early 1900s. Decades of research have improved our understanding of decompression physiology, and the insights incorporated in decompression models have allowed people to dive deeper into the ocean. However, these efforts have not yet, and are unlikely in the near future, to result in a 'universal' deterministic model that can predict when decompression sickness will occur. Divers using current recreational dive computers need to be aware of their limitations. Probabilistic models based on the estimation of parameters using modern statistical methods from large databases of dives offer a new approach and can provide a means of standardisation of deterministic models. Future improvements in decompression practice will depend on continued improvement in understanding the kinetics and dynamics of gas exchange, bubble evolution and tissue response, and the incorporation of this knowledge in risk models whose parameters can be estimated from large databases of human and animal data.