Going Past the Chemist's

In The Guardian's ongoing series of science questions posed to major political parties, the Green Party's response to the question
Is animal testing necessary? Are the ethical concerns outweighed by the benefits? How would you like to see regulations on animal testing change under your government, if at all?
states that

We agree with the independent patient safely organisation, the Safer Medicines Trust, that animal testing may be more harmful than helpful.

The Safer Medicines Campaign, previously known as Europeans for Medical Progress, has a habit of using arguments like the following:

1. Results from other species simply do not reliably translate to the clinic, as evidenced by the 92% failure rate of potential new drugs in clinical trials. [1]
2. That must include animal data, since crucial decisions, such as whether to proceed to clinical trials and whether the drug might cause cancer or birth defects are based on demonstrated safety in animals. Yet, as we know from Northwick Park, even safety in monkeys at enormous doses does not guarantee safety in humans. [1]
3. A large systematic survey published in November 2009 found serious omissions in reporting of data and in strategies to reduce bias in results. Only 12% of the animal studies used randomisation, only 14% used blinding and only 8% gave the raw data. [1]
4. The best way to evaluate the effectiveness of animal tests for drug safety is to compare their results with subsequent real- world outcomes in patients and consumers. [1]
5. Aids is another: while at least 80 vaccines work in animals, all 80 have failed in human trials. Similarly, every one of more than 150 stroke treatments successful in animals has failed in human testing. A study in the British Medical Journal found that animal tests accurately predict human response less than 50% of the time. [2]

I've previously argued that their arguments are weak. Take the first. We're told 92% of the drugs tested that pass animal tests fail in later human clinical trials (the phrasing is mildly ambiguous but I believe that is what is meant). While that might look like a problematic figure, I would think that any competent scientist would also want to know:

1. The proportion of drugs that failed animal tests but would pass a later human clinical trial.
2. The proportion of drugs that failed animal tests but would also fail a later human clinical trial.
3. The proportion of drugs overall that fail animal tests

In other words, we ideally want a test for drugs that perfectly predicts the results of human clinical trials, as we don't want to do clinical trials that needlessly endanger people or might unacceptably delay them getting the best treatment. In the event we don't have that, we need to know four numbers to get a good picture of things - how many drugs failing one test would fail the other (which is a good thing), how many would pass one but fail the other (which would mean we are maybe giving those in clinical trial dangerous substances), how many would fail one but pass the other (meaning we've missed out on a potential cure) and how many pass both (which again is a good thing - these are the drugs that will go into common usage).

Similarly, other quotes above give one piece of information without giving corresponding pieces of information that are crucial in assessing the best options in pharmaceutical testing, and I would argue that "The best way to evaluate the effectiveness of animal tests for drug safety" is not "to compare their results with subsequent real- world outcomes in patients and consumers" but "to compare their results with other potential testing techniques".

I'm continually disturbed by the fact that the Safer Medicines Trust, which says "We focus on evidence based analysis of animal experimentation to assess the balance of help or harm to human health", so frequently writes pieces of publicity that fail to give all the necessary information to do this.

If you think animal testing is morally wrong, that's not something I can really argue with you about, but if you want to make a case it's outdated and there are better options I'd love to hear about them, but I'm usually disappointed by how the case is presented.

I appear to have found myself engaged with @angelneptustar, an astrology fan and also a Boris Johnson fan.

This came about through Marsh's post at the Mersyside Skeptics site. I read through the HuffPo article in question (I refuse to link to that rag, but you can follow through to it if you really wish) where angelneptustar comments on Boris Johnson's astrology chart and how it shows he is very talented.

Browsing my arxiv feeds this morning, I picked up this post - "A defense of Columbo (and of the use of Bayesian inference in forensics): A multilevel introduction to probabilistic reasoning" by G. D'Agostini.

Considering the topic, it's an enjoyable read. It's motivated by this NewScientist article from late last year. I criticised that article myself but did not touch upon the incident with Columbo. D'Agostini provides a fairly clear if mildly technical explanation of the mathematics behind the problem expressed - of the strength of the evidence against the killer Columbo caught when he picked one of thirteen (or twelve, as the NewScientist article says, but that's a minor niggle) cameras off a shelf, taking the one involved in the crime. As NewScientist says:

If only it were that simple. Killer or not, anyone would have a 1 in 12 chance of picking the same camera at random. That kind of evidence would never stand up in court.

Not so long ago, the Science and Technology delivered their report on homeopathy. It wasn't to homeopaths tastes. If you haven't heard of the story, I recommend Martin Robbins' piece for the Guardian.

The British Homeopathic Association has felt the need to respond. I thought I would take a look at parts of it.