I just sorta worry if there is correct culture fit in pharmaceuticals when there is a prevailing mentality of "no one ever got fired for buying Microsoft".
Let us talk about "disruptive innovations". What if I told you I was part of the team that approved a treatment for H1N1 flu pandemic in children without ANY trial based on unapproved data in adults? What if I told you the development pathway for pulmonary arterial hypertension in pediatrics was driven by analysis performed by Pumas-like software to establish a reasonable endpoint? These are patients who cannot perform most daily functions we take for granted. There would have been no drugs approved otherwise. How can you, me and others contribute to the disruptive innovation which will transform how drugs are developed and patients are treated. Pumas is designed to disrupt drug development and precision medicine. There are plenty of opportunities for disruption in healthcare. these opportunities are the motivation for Pumas..Joga Gobburu (Co-Founder, Pumas-AI)
Pumas and the Julia ecosystem are about integrated modeling speak across multiple sectors that are composable and the best part is that one does not have to develop in one environment and switch to another for production. Your point about Salesforce may be right for trial operations, but someone has to do the anlaytics that connect back to the first principles of physiology and pharmacology. Pumas provides this connection to first principles from the core, and lets you build on it for both internal decision making or to serve the stakeholders with fancy UI's.
A subtle but important point here is that compared to a 30-40 years ago since when SAS became a mainstay, I would argue that more of the younger generation are not averse to coding. In fact programming at the basic level is a fundamental expected skill set, and I personally see more and more life-science majors be efficient coders, thanks to the democratization of tools and education. Combined with this new workforce, and tools like Pumas and Julia, we are not far away from disrupting this sector.
By choosing a language that a tiny minority know and use, haven't you crippled your adoption from the start?
This is a very clear case of a well-defined application. The field of pharma needs a combination of mechanistic models (based on differential equations), statistics, and ability to work with large datasets. The existing tools are far from sufficient. If anything, the need for high performance and scalable tools has been felt in the industry for a very long time.
There are hundreds of thousands of Julia programmers worldwide now, and the user base is doubling every year. After all, new ideas do start with a tiny minority, but one that truly believes in those ideas and is enthusiastic in adopting them early.
So you could still use python and just wrap Julia libraries for those operations it's optimized fors
> FWIW, other software in this field have essentially built mini JIT compilers for Fortran
Things like LLVM not specialist enough in terms of optimizations?
The reason why companies have to hold on to their SAS and COBOL is that once a drug is out in the real world, and there is a lawsuit, or a recall - you need to be able to have everything available for review. These things can happen 20 or 30 years after a drug becomes available. That would naturally induce risk aversion.
New drugs today will use newer tools, and 30 years out, we'll be saying the same things about today's tools. In fact pharma companies have no choice but to adapt. Technology moves fast, and anything that gives an edge, if not adopted, will leave you behind.
Also is your stack open source? I somehow doubt the long-term advantage of one proprietary stack over another.
One of the reasons it is still popular is for regulatory use it's very well understood.
I'm not enough of an expert to answer the second part of your question - on why new numerical stacks are beneficial.
It may be around building more complex models of PK/PD but I'm not up to speed on what winnonlin can and can't do.
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.
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.
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.
How would you go about that?
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.
Pumas is at the other end - modelling and simulation tool that simulates the pharmacokinetics and pharmacodynamics - effects of a drug on the body and the body on the drug. It is relevant during clinical trials, for dosing and for final submission to the regulator.
There is a lot of complexity in drug dosing, but it is possible to eventually bring the modelling tools to the patient's bedside and personalize the dose to have effective treatment. There are also lots of possibilities for new treatments by mining old trials, and by learning new therapies from old trial datasets - what the FDA now calls Real World Evidence.
While I am a co-founder of Julia Computing, we are the technology partners for Pumas.ai, and Pumas is one of the most complex and largest Julia applications to date leveraging a large part of the Julia ecosystem.