Week 2: Literature, Data, and Learning the Tools

Hello everyone! Welcome to my second blog for this senior project, I’m so glad to see you all again!

This week, my main focus was on performing a thorough reading of existing literature to see how much of it I could use and the work required of me in order to analyze it, along with beginning to learn the computational tools I’ll need.

My original list of papers turned out to have very limited useful data, but luckily, I chanced upon a paper that had collected and referenced numerous other articles, and from that I gathered 8 experiments that met the criteria: oral psilocybin administration, healthy participants with reported ages and sample sizes, no substance abuse history, have both effect against time and concentration against time data, and most importantly, must have the participant’s subjective effects measured using the Visual Analog Scale (VAS). I was originally prepared to convert between subjective effect scales, but seeing how commonly used the VAS was I decided it was a good standardized scale to settle on. The only practical problem is that some of the papers don’t use the exact same VAS, as in some report in 0-10 while others report in 0-100%, so I’ll need to normalize this data to a common scale first.

Combined, these 8 papers provide me with data from 189 participants who received psilocybin doses ranging from 3mg to 30mg. I will be using all of this data, as my model maps psilocin concentration directly to the effect, independent of dose, and having a wide variety of data strengthens the curve fit. The low-dose subjects secure the bottom of the sigmoid curve, where effects are just emerging, while the high-dose subjects define the upper area. I mention sigmoid curve since that is what I predict the final model to take the form of, as drug effects typically follow the pattern of a slow onset at lower concentrations, an incredibly steep rise in the middle range, before a plateau at the higher concentrations.

On the technical side of things, I’ve begun learning two tools this week. WebPlotDigitzer is a browser-based tool that I’m hoping to rely on for data extraction. Most of the data I currently have is in the form of plots, and I will need to get it into a table format. I’ve practiced this so far on the data from Madsen et al. (2019) which show data from 8 subjects. Extracting the data from the two graphs and matching their times gave me my first real paired dataset. I’ve also begun working on tutorials for the SciPy library, mainly focusing on solve_ivp, which I believe will be at the core of my project. It solves differential equations, which is what a PK model is. I followed these tutorials and tested my knowledge by running the code in Google Colab to form simulated concentration-time curves and matching them with the data in published papers.

Next week, I will begin data extraction across the 8 papers and hopefully create the master dataset that will form the basis of my project. See you all then!