Abstract

BackgroundIntrathecal drug delivery has a significant role in pain management and central nervous system (CNS) disease therapeutics. A fluid-physics based tool to assist clinicians in choosing specific drug doses to the spine or brain may help improve treatment schedules.MethodsThis study applied computational fluid dynamics (CFD) and in vitro model verification to assess intrathecal drug delivery in an anatomically idealized model of the human CSF system with key anatomic features of the CNS. Key parameters analyzed included the role of (a) injection location including lumbar puncture (LP), cisterna magna (CM) and intracerebroventricular (ICV), (b) LP injection rate, injection volume, and flush volume, (c) physiologic factors including cardiac-induced and deep respiration-induced CSF stroke volume increase. Simulations were conducted for 3-h post-injection and used to quantify spatial–temporal tracer concentration, regional area under the curve (AUC), time to maximum concentration (Tmax), and maximum concentration (Cmax), for each case.ResultsCM and ICV increased AUC to brain regions by ~ 2 logs compared to all other simulations. A 3X increase in bolus volume and addition of a 5 mL flush both increased intracranial AUC to the brain up to 2X compared to a baseline 5 mL LP injection. In contrast, a 5X increase in bolus rate (25 mL/min) did not improve tracer exposure to the brain. An increase in cardiac and respiratory CSF movement improved tracer spread to the brain, basal cistern, and cerebellum up to ~ 2 logs compared to the baseline LP injection.ConclusionThe computational modeling approach provides ability to conduct in silico trials representative of CSF injection protocols. Taken together, the findings indicate a strong potential for delivery protocols to be optimized to reach a target region(s) of the spine and/or brain with a needed therapeutic dose. Parametric modification of bolus rate/volume and flush volume was found to have impact on tracer distribution; albeit to a smaller degree than injection location, with CM and ICV injections resulting in greater therapeutic dose to brain regions compared to LP. CSF stroke volume and frequency both played an important role and may potentially have a greater impact than the modest changes in LP injection protocols analyzed such as bolus rate, volume, and flush.

Highlights

  • Intrathecal drug delivery has a significant role in pain management and central nervous system (CNS) disease therapeutics

  • Tracer distribution rate in the caudocranial direction was slower within the lumbar puncture (LP) injection compared to cisterna magna (CM) and ICV

  • Importance of physiological variables on tracer spread Variation of physiological variables, namely cardiacinduced and respiratory-induced Cerebrospinal fluid (CSF) stroke volume and frequency, were found to have a stronger impact on tracer transport to the brain compared to a 5X bolus rate, 3X bolus volume, or adding a 5 mL flush. These results indicate that CSF stroke volume and frequency both play an important role and may potentially have a greater impact than modest changes in LP injection protocols such as bolus rate, volume, and flush

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Summary

Introduction

Intrathecal drug delivery has a significant role in pain management and central nervous system (CNS) disease therapeutics. CSF has a viscosity and density similar to water at body temperature and resides in the subarachnoid space (SAS) and ventricles of the brain with a total volume ranging from 250 to 400 mL in healthy adult humans [1, 2]. Injection of tracers into the CSF and computational modeling has shown that slow “steady-streaming” CSF velocities, on the order of ~ 0.1 mm/s, lead to net movement of tracers throughout the CSF system [6]. This transport can be observed by cintigraphic imaging [7], positron emission tomography [8], and contrast enhanced magnetic resonance imaging using gadolinium [9]. The exact mechanism(s) by which this transport takes place has been hypothesized to be due to fluid advection, various types of diffusion, and/or fluid structure interaction [11]

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