Nilotinib is a broad‐based tyrosine kinase inhibitor with the highest affinity to inhibit Abelson (c‐Abl) and discoidin domain receptors ( DDR 1/2). 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 (pop PK / 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.

Physiologically based pharmacokinetic (PBPK) models have been developed describing the disposition kinetics of nicotine and its major metabolite, cotinine, in man. Separate 9-compartment, flow-limited PBPK models were initially created for nicotine and cotinine. The physiological basis for compartment designation and parameter selection has been provided; chemical-specific tissue-to-blood partition coefficients and elimination rates were derived from published human and animal data. The individual models were tested through simulations of published studies of nicotine and cotinine infusions in man using similar dosing protocols to those reported. Each model adequately predicted the time course of nicotine or cotinine concentrations in the blood and urine following the administration of nicotine or cotinine. These individual models were then linked through the liver compartments to form a nicotine-cotinine model capable of predicting the metabolic production and disposition of cotinine from administered nicotine. The potential for integrating this functional PBPK model with an appropriate pharmacodynamic model for the characterization of nicotine's physiological effects is discussed.

The application of mathematical models to reflect the organization and activity of biological systems can be viewed as a continuum of purpose. The far left of the continuum is solely the prediction of biological parameter values, wherein an understanding of the underlying biological processes is irrelevant to the purpose. At the far right of the continuum are mathematical models, the purposes of which are a precise understanding of those biological processes. No models in present use fall at either end of the continuum. Without question, however, the emphasis in regards to purpose has been on prediction, e.g., clinical trial simulation and empirical disease progression modeling. Clearly the model that ultimately incorporates a universal understanding of biological organization will also precisely predict biological events, giving the continuum the logical form of a tautology. Currently that goal lies at an immeasurable distance. Nonetheless, the motive here is to urge movement in the direction of that goal. The distance traveled toward understanding naturally depends upon the nature of the scientific question posed with respect to comprehending and/or predicting a particular disease process. A move toward mathematical models implies a move away from static empirical modeling and toward models that focus on systems biology, wherein modeling entails the systematic study of the complex pattern of organization inherent in biological systems.

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