Drug discovery is fairly different from pharmacometrics. Drug discovery is about finding what chemicals would likely produce the right effects by mining models and simulation for how they would bind to proteins and how that effects the protein's behavior. This generally uses molecular simulations, things like molecular dynamics or DFT to compute properties of the molecules themselves.
Pharmacometrics is focused on precision dosing: given a drug in a clinical trial, how should you be personalizing the dosing in order to have high efficacy with low toxicity? This is different depending on many factors (weight, metabolic factors, gender, etc.) and are a mix systems physiology types of models of metabolic and cell signaling (quantitative systems pharmacology and physiologically-based pharmacokinetics) and compartmental models.
They are both useful, just at different stages of the drug development pipeline. Drug discovery modeling and simulation is done at the very early stages before the clinical trial to predict what drugs to test and what the specificity of the targeting is (i.e. will it have off-target effects and cause side effects?). On the other hand, pharmacological modeling and simulation is done during the clinical to try and adaptively change the dosing, understand effects on the population, and predict whether the new off-target effects cause a system-wide toxic effects (i.e. just because drug X accidentally blocks the binding of Y to Z doesn't necessarily mean that most people will have a side effect, but you can predict whether certain sub-populations might be more prone to side effects and how likely that is to cause a clinical trial to fail). Given the cost of clinical trials is in the billions, any mathematics that can predict whether it will fail or simply avoid a clinical trial by proving safety through statistical means is something that's in high demand.
Hey Chris, thanks for the great talks on math and ML with Julia. I highly recommend them to anyone interested in learning how to spell mathematical model in julia or, in general, in any language.
Do you really think you can avoid a trial by proving safety through statistical means?
Aren't clinical trials done because we don't know in advance - as the full complexity of biology is beyond our ability to predict?
ie we do the trials to find out the things we didn't know ( and thus couldn't model ).
Perhaps through rationalization of a trial result to avoid the call for additional trials - but I find it hard to believe trials can be avoided in general.
I think the key "emergency use" is missed. The question is what are we going to do when you need something right away in a pandemic situation. The point here has nothing to do with "avoiding" trials - that I will never advocate. But there are enough situations that require us to think "outside the box". at that point, magic cannot happen. we need build things systemically as science progresses...
Even in a pandemic situation, I'd not sure I'd ever use a model if I could do a phased rollout in terms of safety.
And if it's so bad - that you have no option but to give it to everyone now - well you have no option...
I suppose the practical problem right now is not so much about the risks of one individual vaccine, but rather choosing between the many many candidates.
Out of the categories of RWE - I'd say that the real time patient data collection looks the most promising - but then in some ways that's just clinical trial information collected in a different way.
Pharmacometrics is focused on precision dosing: given a drug in a clinical trial, how should you be personalizing the dosing in order to have high efficacy with low toxicity? This is different depending on many factors (weight, metabolic factors, gender, etc.) and are a mix systems physiology types of models of metabolic and cell signaling (quantitative systems pharmacology and physiologically-based pharmacokinetics) and compartmental models.
They are both useful, just at different stages of the drug development pipeline. Drug discovery modeling and simulation is done at the very early stages before the clinical trial to predict what drugs to test and what the specificity of the targeting is (i.e. will it have off-target effects and cause side effects?). On the other hand, pharmacological modeling and simulation is done during the clinical to try and adaptively change the dosing, understand effects on the population, and predict whether the new off-target effects cause a system-wide toxic effects (i.e. just because drug X accidentally blocks the binding of Y to Z doesn't necessarily mean that most people will have a side effect, but you can predict whether certain sub-populations might be more prone to side effects and how likely that is to cause a clinical trial to fail). Given the cost of clinical trials is in the billions, any mathematics that can predict whether it will fail or simply avoid a clinical trial by proving safety through statistical means is something that's in high demand.