Looking at the new Covid treatment Paxlovid (nirmatrelvir + ritonavir), how does it work and can we work out summary statistics? Then, can we compare them to the ones we worked out for molnupiravir last time? What are the problems with this approach, and why do we need to be cautious comparing outcomes between different trials? (Note: at the time of recording, there wasn't much guidance on how to pronounce nirmatrelvir, so I've gone for NIR-ma-TREL-vir rather than NIR-MAT-rel-vir or any other version. It's a best guess, so sorry if I've gone for the protease inhibitor version of CLO-pi-DOG-rel. Only time will tell)

What do you need to think about when giving medicines via enteral feeding tubes? What are theoretical problems vs real problems, how do you balance the needs of the medicine with the needs of the patient, and how are you going to come to a decision? This podcast isn't going to tell you all the answers, but will hopefully give you pointers on what you might need to consider.

Why do so many medicines taste disgusting, and what do industry try to do to fix this? And more importantly, what can we do to make taking (or giving) medicines less of an ordeal? There's no profound learning in this one, but you might find out why bribing with chocolate is the most successful strategy for getting medicines into children.

Using molnupiravir as an example again, we look at the new data that's been released and the FDA's analysis. Why is the data released showing something different to the interim analysis only a fortnight or so ago? And why does this seem to happen a lot, with early results being more positive than the later full results? And what does this mean for us and how we think about using early data?

We use QALYs to judge how good treatments are and compare them to other treatments, but what are their limitations? Can we use the recent discussions about whether teenagers should be vaccinated against Covid to illustrate these things about QALYs? Listen to find out how successfully we've done this!

Using molnupiravir as an example, and looking at the data from news reports rather than papers, can we work out summary statistics for how well it might work, how can we represent them honestly, and how might we communicate them effectively to others?

What are we trying to do when we do therapeutic drug monitoring? How do we do it, and what can we do if levels aren't taken exactly how we'd like? A pragmatic review of the first principles involved in therapeutic drug monitoring from a pharmacy perspective

A very brief overview of what it means if we say a medicine is unlicensed - what does it mean, and what do we need to do differently compared to if we're using a licensed medicine?

Medicines in secondary care in England are funded in confusing ways. This podcast tries to give a brief overview of why different medicines are funded differently, how you can predict who might pay for different medicines, and how it is kept relatively fair between different providers

How and why do we use summary statistics to explain clinical trials, and why are some statistics better than others for helping us understand what a drug might do? In this podcast, we talk about absolute risks, absolute risk reductions, and number needed to treat, and why they tend to be better statistics to use for understanding compared to odds, odds ratios and relative risk reductions.