Browsing by Author "Adrianzen Alvarez, Daniel Roberto"

Computational fluid dynamic (CFD) studies of blood flow in cerebrovascular aneurysms have potential to improve patient treatment planning by enabling clinicians and engineers to model patient-specific geometries and compute predictors and risks prior to neurovascular intervention. However, the use of patient-specific computational models in clinical settings is unfeasible due to their complexity, computationally intensive and time-consuming nature. An important factor contributing to this challenge is the choice of outlet boundary conditions, which often involves a trade-off between physiological accuracy, patient-specificity, simplicity and speed. In this study, we analyze how resistance and impedance outlet boundary conditions affect blood flow velocities, wall shear stresses and pressure distributions in a patient-specific model of a cerebrovascular aneurysm. We also use geometrical manipulation techniques to obtain a model of the patient’s vasculature prior to aneurysm development, and study how forces and stresses may have been involved in the initiation of aneurysm growth. Our CFD results show that the nature of the prescribed outlet boundary conditions is not as important as the relative distributions of blood flow through each outlet branch. As long as the appropriate parameters are chosen to keep these flow distributions consistent with physiology, resistance boundary conditions, which are simpler, easier to use and more practical than their impedance counterparts, are sufficient to study aneurysm pathophysiology, since they predict very similar wall shear stresses, time-averaged wall shear stresses, time-averaged pressures, and blood flow patterns and velocities. The only situations where the use of impedance boundary conditions should be prioritized is if pressure waveforms are being analyzed, or if local pressure distributions are being evaluated at specific time points, especially at peak systole, where the use of resistance boundary conditions leads to unnaturally large pressure pulses. In addition, we show that in this specific patient, the region of the blood vessel where the neck of the aneurysm developed was subject to abnormally high wall shear stresses, and that regions surrounding blebs on the aneurysmal surface were subject to low, oscillatory wall shear stresses. Computational models using resistance outlet boundary conditions may be suitable to study patient-specific aneurysm progression in a clinical setting, although several other challenges must be addressed before these tools can be applied clinically.

Our lab focuses on developing and optimizing drug delivery systems for applications in women’s health. In this field, development of drugs and drug delivery systems is hindered by a heavy reliance on empirically derived data, usually obtained from non-standardized, highly variable in vitro and in vivo animal experiments. Further, without a mechanistic understanding of the various phenomena progressing during drug delivery, experiments tend to explore complex parameter spaces blindly and randomly. Deterministic mathematical models can improve the efficiency of this process by informing rational drug and product design. In this work, we were interested in two applications: 1. drug delivery of topically applied anti-HIV microbicides to the female reproductive tract; and 2. Localized intratumoral injections of ethanol-ethyl cellulose mixtures for treatment of cervical lesions. Development of topically applied anti-HIV microbicides to prevent sexual HIV transmission is inefficient, with in vitro and in vivo tests having limited applicability to real product use. This issue is exacerbated by the dependence of drug performance on adherence and drug-administration conditions, which are not tested until clinical trials. Further, the lack of a standardized pharmacodynamic (PD) metric that is dependent on the heterogenous dynamics of viral transport and infection makes it difficult to identify the most promising drug candidates. Here we develop a deterministic mathematical model that incorporates drug pharmacokinetics (PK) and viral transport and dynamics to estimate the probability of infection (POI) as a PD metric that can be computed for a variety of anti-HIV drugs in development. The model reveals key mechanistic insights into the spatiotemporally dependent dynamics of infection in the vaginal mucosa, including susceptibility to infection at different phases in the menstrual cycle. Further, it and can be used as a platform to test novel drugs under several conditions, such as the timing of drug administration relative to the time of HIV exposure. Localized injections of ablative agents, immunotherapeutics and chemotherapeutics have potential for increased therapeutic efficacy against tumors and reduced systemic effects. However, injection outcomes thus far have been largely unsatisfactory, due to unintended leakage of the active pharmaceutical ingredients (APIs) to non-target tissues. Adding a gelling or precipitating agent to the injection can help ameliorate this limitation, by acting to contain the API within the target tissue. One such example is injection of ethanol-ethyl cellulose mixtures. Due to the insolubility of ethyl cellulose in water, this polymer phase-separates in the aqueous tumor environment, forming a fibrous gel that helps contain ethanol, the current ablating agent (and chemotherapeutic drugs in the future), within the boundaries of the tumor. Our collaborators have shown that this strategy can be an effective low-cost treatment strategy for superficial solid tumors, with cervical cancer and cervical dysplasia, and liver cancer, being promising targets. Here we present a mathematical model that enables characterization of the injection process. Our model uses Cahn Hilliard theory to model the phase separation of a precipitating or gelling agent during injection into poroelastic tissue. This theory is linked to the soft mechanics of tissue deformation during the injection, and to mass transport theory for the API. The model predicts key elements of the injection process, including the pressure field, the soft tissue displacement field, the phase constitution of the precipitating or gelling agent in the tissue, and the concentration distribution of the API in the tissue. The model enables us to explore relationships between these elements and fundamental injection and tissue parameters. This can inform design of optimized injection protocols. Select model predictions include that larger injection volumes do not significantly affect cavity volumes but do lead to faster transport of the API to target tumor tissue. However, although higher flow rates lead to larger cavities – in the absence of tissue fracture, and when injected volume is held constant – they also lead to slower delivery of the API into the target tumor tissue. This is due to the shorter injection times. Importantly, concentration distributions of the API are not sensitive to the speeds of precipitation of the precipitating agents or to diffusion coefficients of the API in the dense (gelled) phase of the injectate material. The model presented here enables first-pass exploration of injection parameter space for select tissue types (properties). This can aid in optimization of localized therapeutic injections in a range of applications.