PyCon 2019 in Cleveland, Ohio

Sunday 10 a.m.–1 p.m. in Expo Hall

Simulation model for 3D-printed scaffold development for personalised medicine using Python

Hassan Sami Adnan, Atia Amin, Haque Ishfaq, Hassan Saad Ifti, Samara Sharmeen


Complimentary to oral drugs, personalised 3D-printed formulations can add value to patient demands where the release of the drug can be controlled and optimised to the patient’s health needs. Research is being done to establish drug delivery mechanisms that provide sustained- and controlled-release profiles of active pharmaceutical ingredients using 3D printed scaffolds and similar additive manufacturing. Designing such scaffolds and prototypes can be time-consuming and costly given the novel approach and emerging equipments and technologies that are necessary. Furthermore, failed prototypes cannot be changed once printed limiting researchers to try out different configurations or change other design factors. The required wall-topology of the scaffolds is dependent on the fluidic behaviour of the inner ingredients in liquid form whilst exiting through the scaffold wall. A practical method to drive this releasing process is to store the inner liquid at a higher pressure than the average ambient pressure in the stomach. The rate at which the liquid is released is determined by this pressure differential and the wall-topology, i.e. the passage diameter, length, surface-roughness, and structure. This fluidic behaviour can be modelled by a one-dimensional equation where the pressure differential is proportional to the releasing rate of the liquid-volume. The factor of proportionality here is intrinsically dependent on the wall-topology. Hence, determining this factor for a given pressure differential and releasing rate of the drug can directly suggest the required wall-topology for the scaffold. This model will instantly enable researchers to start testing their designs for effectiveness and efficiency before the prototype is printed, thus decrease waste, financial burden, and time consumption. To our knowledge, this is the first time that this approach has been taken. At PyCon, we foresee to present our model in form of a poster. The model is simulated using Python and the Gauss-Seidel algorithm. This model further demonstrates a novel combination of the field of medicine and fluid dynamics, where Python—as an open source language—acts as a viable bridge. We believe this will showcase the limitless possibilities of Python and enable us to connect with similarly motivated Python enthusiasts.