Arrhythmic risk: regression, single cell biophysics, or big tissue simulations?

On the 15th March I presented at an FDA public Advisory Committee hearing on the proposals of the CiPA initative. CiPA aims to replace the current testing for increased drug-induced Torsade de Pointes (TdP) arrhythmic risk, which is a human clinical trial, with earlier pre-clinical testing (including mathematical modelling) that could give a more accurate assessment without the need for a human trial.  Most of my talk (available on Figshare) was about the rationale for using a biophysically-based mechanistic model to classify novel compounds’ TdP risk, the history of cardiac modelling, and how simulations might fit into the proposals.

The advisory committee asked some great questions, and I thought it was worth elaborating on one of my answers here. To summarise, quite a few of their questions came down to “Why don’t you include more detail of known risk factors?”. Things they brought up include:

  • long-short-long pacing intervals is often observed in the clinic prior to Torsade de Pointes starting, why not include that?;
  • should we model Purkinje cells rather than ventricular cells (perhaps ectopic beats or arrhythmias arise in the Purkinje fibre system)?;
  • heart failure is a known risk factor – would modelling these conditions help?

Mechanistic markers

Before answering, it’s worth considering where we are now in terms of ‘mechanistic’ markers of arrhythmic risk. Figure 1 shows how things are assessed at the moment.

Fig 1: block of hERG -> prolongation of action potential duration (APD) -> prolongation of QT interval on the body surface ECG. Taken from my 2012 BJP review paper.

It was observed that the risky drugs withdrawn from the market in the late 90s prolonged the QT interval, and for these compounds this was nicely mechanistically linked to block of the hERG channel/IKr (top panel of Fig 1). This all makes nice mechanistic sense – a prolonged QT is related to delayed repolarisation (as in bottom of Fig 1), which in turn is related to block of hERG/IKr.

There’s a couple of reasons prolonged repolarisation is conceptually/mechanistically linked to arrhythmia. Firstly, if you delayed repolarisation ‘a bit more’ (continue to decrease the slope at the end of the action potential – middle panel of Fig 1), you’d get repolarisation failure, or after-depolarisations. Secondly, by delaying repolarisation you may cause regions of tissue to fail to be ready for the following wave, termed ‘functional block’.

As a result of the pathway from hERG block to QT prolongation, early ion channel screening focusses on checking compounds don’t block hERG/IKr. The clinical trials tried to avoid directly causing arrhythmias, for obvious reasons, but by looking for QT prolongation in healthy volunteers you would hopefully spot compounds that could have a propensity to cause arrhythmias in unhealthy heart tissue, people with ion channel mutations, people on co-medication with other slightly risky compounds, or other risk factors. This has been remarkably successful, and there have very few surprises of TdP-inducing compounds sneaking past the QT check without being spotted.

But, there were some hERG blockers on the market that didn’t seem to cause arrhythmias. Our 2011 paper showed why that can happen –  there are different mechanistic routes to get the same QT or APD changes (by blocking multiple ion channels rather than just hERG) and if you took multiple ion channel block into account you would get better predictions of risk than simply using the early hERG screening results. So multiple ion channel simulations of single cell APD are a very similar idea to clinical trials of QT (and comparing the two is a good check that we roughly understand a compound’s effects on ion channels).

So clinical QT/simulated APD is a mechanistically-based marker of arrhythmic risk, but we know it still isn’t perfect because some drugs with similar QT prolongation in our healthy volunteers have different arrhythmic risks (see CiPA papers for an intro).

One extreme: as detailed as possible

At one end of the scale, some studies advocate whole-organ simulations of TdP in action to assess TdP risk. Here’s a video of the impressive UT-heart simulator from Tokyo that was used in that study.

These simulations definitely have their place in helping us understand the origin of TdP, how it is maintained/terminates, and possibly helping design clinical interventions to deal with it. If we want to go the whole hog and really assess TdP risk completely mechanistically why not do patient-specific whole organ TdP simulations, with mechanics, individualised electrophysiology models, all the known risk factors, the right concentrations of compound, and variations of these throughout the heart tissue, etc. etc.?

Let’s imagine for a minute that we could do that, and got a model that was very realistic for individual patients, and we could run simulations in lots of different patients in different disease states, and observe spontaneous initiation of drug-induced TdP via the ‘correct’ mechanisms (this hypothetical situation ignores the not-inconsiderable extra uncertainties in how well we model regional changes in electrophysiology, what changes between individuals, blood pressure regulation models, individual fibre directions, accurate geometry, etc. etc. etc. which might mean we get more detail but less realism than a single cell simulation!). Let’s also say we could get these huge simulations to run in real time – I think the IBM Cardioid software is about the fastest, and goes at about three times less than real time on the USA’s biggest supercomputer.

That would be brilliant for risk assessment wouldn’t it?

Unfortunately not!

TdP is very rare – perhaps occurring once in 10,000 patient-years of dosing for something like methadone. Which means ultra-realistic simulations would have to run for 10,000 years just to get an N=1 on the world’s biggest supercomputer! It’s going to be quite a while before Moore’s law makes this feasible…

Inevitably then, we are not looking to model all the details of the actual circumstances in which TdP arises, we’re looking for some marker that correlates well with the risk of TdP.

The other extreme: as simple as possible

The other extreme is perhaps forgetting about mechanism altogether, and simply using a statistical model, based on historical correlations of IC50s with TdP, to assess the risk. Hitesh Mistry did something a bit like this in this paper (although as I’ve said at conferences – it’s not really a simple statistical correlation model, it’s really a clever minimal biophysically based model, since it uses Hill equation and balance of depolarising and repolarising currents!). But for two or three ion channel block it works very well.

Why would I like something a bit more mechanistic than that then? I came up with the example in Figure 2 to explain why in the FDA hearing.

statistical_vs_biophysical

Fig 2: imagine dropping a mass of 1kg off a tower and timing how long it takes to fall to the ground. You might get the blue dots if you did it from between the second and third floors of a building. Left: if you were a statistician only you might then do a linear regression to estimate the relationship between height and time (think ion channel block and TdP risk). Right: a physicist would get out Newton’s II law and derive the relationship on the right. The one on the left would be dodgy for extrapolating outside the ‘training data’, the one on the right would be fairly reliable for extrapolating (not completely – as it doesn’t include air resistance etc.!)

Why might ion channel block be like Fig 2? Well when you’re considering just two or three channels being blocked, then Hitesh’s method (which actually includes a bit of mechanistic Newton’s II law in my analogy!) will work very well, assuming it’s trained on enough compounds of various degrees of block of the three channels, as the blue dots will cover most of the space.

But you might want to predict the outcome of block (or even activation) up to seven or more different ionic currents (and combinations thereof) that could theoretically happen and cause changes relevant to TdP risk. In this case, any method that is primarily based on a statistical regression, rather than mechanistic biophysics, is going to struggle because of the curse of dimensionality. In essence, you’ll struggle to get enough historical compounds to ‘fill the space’ for anything higher than two or three variables/ion currents. You could think of the biophysical models as reducing the dimension of the problem here (in the same way as the biology does, if we’ve got enough bits of the models good enough), so they can output a single risk marker that is then suitable for this historical correlation with risk – without a huge number of compounds.

The right balance?

CiPA is pursuing single-cell action potential simulations, looking for markers of arrhythmic risk in terms of quantifying ‘repolarisation stability’ in some sense. I think this is a very sensible approach, geared simply at improving one step on solely APD/QT.

In terms of including more risk factor details in here, as the committee asked originally at the top of this post, the real question is ‘does it improve my risk prediction?‘ or not. Hopefully I’ve explained why including all the detail we can think of isn’t obviously going to help. Your ranking of a new compound in terms of the risk of established ones would have to change in order for a new simulated risk marker to make any difference.

To assess whether that difference really was an improved risk prediction we would need to have faith that risk factors were widely applicable to the people who are getting TdP, and that any changes to the models for introducing heart failure etc. are sufficiently well validated and trusted to rely on them. I don’t think we are quite ready for this, as there is plenty to do at the moment trying to ensure there is an appropriate balance of currents in a baseline model (before any are blocked – two relevant papers: paper 1, paper 2), and that kinetics of drug-block of hERG included, as these are probably important.

Another thought along the committee’s lines is TdP risk for different patient subgroups, instead of a one-size-fits-all approach. This would be very nice, but the same difficulties apply, multiplied! Firstly, getting models that we trust for all these subgroups, with well quantified levels/distributions of ion channel expression and other risk-factor-induced changes. Secondly, even sparser gold standard clinical risk categorisation for all subgroups to test our models on. Unfortunately, with such a rare side effect it is difficult enough to get an overall risk level, never mind risk tailored to individual subgroups. So at present, I think the CiPA proposal of a single cell model (give or take an additional stem-cell derived myocyte prediction perhaps!) and single risk marker is a very sensible first step.

As usual, comments welcome below!

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One Response to Arrhythmic risk: regression, single cell biophysics, or big tissue simulations?

  1. Tarachopoiós says:

    Hi Gary, as always a very thought provoking blog post. A few thoughts come to mind (they don’t need a reply as there are many)…

    1) Interpolation v extrapolation: it may be that a regression model out-performs a biophysical model on large data-sets (Mistry et al. showed that this could well be the case), at that point should a regression model be preferred over a biophysical model for compounds that look similar so for interpolation? Surely it must if it performs better?
    2) Also if a regression model out-performs the biophysical model what faith can you have in the extrapolation predictions from the biophysical model? Is it not better sometimes to just do the experiment for a question a model cannot answer rather than developing a model that can answer many but be very imprecise and biased? Does using a simpler model actually make you less risk-averse?
    3) I think its unknown what the frequency of drugs that effect ion-channels other than just hERG is in a typical drug development pipeline. From the current publications it seems that a drugs affinity for more than 2 channels is quite rare. So does the comment about the curse of dimensionality actually apply here if the number of drugs that actually have 2 or more multi-channel effects are as rare as the event of interest?

    In the end it may be that different scales of a model co-exist. This has clearly helped in other fields and maybe it is something we should promote more within the life-sciences.

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