In our blog introducing bioequivalence, we described how studies establishing bioequivalence can be a way to avoid expensive clinical trials without compromising on the safety or efficacy of a drug product. In particular, this applies for generic versions of already approved drugs, or for different dosage routes for those drugs. In these cases, bioequivalence studies…
Bioassays can be expensive, both in terms of time and money. It is, therefore, sensible to consider bioassay optimisation: ways to design assays which minimise resource use while maximising throughput. This derives from the principle of lean manufacturing: processes which reduce waste while simultaneously increasing productivity and maintaining effective procedures. What is optimisation in bioassay?…
In parts 1 & 2 of our series on the confidence interval, we have covered some of the basic statistical concepts – means, standard deviations, and distributions – and how they can be used to describe the results obtained from a bioassay. Then, we described how we can investigate the distribution of our sample-based estimates…
Validation of statistical software is required for use in regulatory work. Many auditors expect to see the traditional IQ, OQ, PQ approach applied to the base system (SAS, R, etc). This approach, however, was never mandated by the FDA – or anyone else – and was never really suited to software validation because it ignores…
A common discussion we at Quantics have with our clients surrounds the issue of pseudo-replicates. It’s an area of tension between scientist and statistician – pseudo-replicates reduce the cost of an assay, but also its statistical validity, meaning that a compromise solution is often required. One such solution is averaging pseudo-replicates, which comes with its…
The United States Pharmacopeia (USP) guidance on biological assay validation, General Chapter <1033>, will soon be updated. In the leadup to the publication of the new guidance, Quantics co-founder Dr Ann Yellowlees has presented on the changes at the recent BEBPA conferences in both the US and Europe. Here, we summarise some of the most…
In part 1 of our series covering the confidence interval, we examined some of the basic statistical concepts associated with a bioassay. In particular, we saw that a property of interest – we considered (and will continue to consider!) the EC50 of a test lot – follows a distribution defined by a mean (\(\mu\)) and…
One of the greatest challenges in the development of new drugs and therapeutics is that it’s just plain expensive. The average spend on a drug which passes clinical trial is on the order of billions of dollars – and don’t forget that more than 90% of drug candidates don’t make it to market – meaning…
A crucial acknowledgement of the scientific method is that no measurement will ever be perfect: results will always have associated variability, no matter how sophisticated the methodology used. Bioassay is no exception, so it is not surprising that one of the most common areas we address at Quantics is how to account for this uncertainty….
Crucial in understanding the safety and efficacy of a drug are how it percolates through the body, or pharmacokinetics (PK), and how the body responds to its presence, or pharmacodynamics (PD). As with any aspect of drug development, testing PK and PD requires collecting data, and, as with all data, interpreting that data requires statistics….
Unless you’ve been hiding under a rock for the past few months (and, honestly, who could blame you), you will have interacted with artificial intelligence (AI) in some form. Whether it’s using ChatGPT to write an email to that one client you wish would go to your competitor or bemoaning the vagaries of the TikTok…
In the most basic sense, relative potency bioassays consist of a comparison of the behaviour of a reference standard to that of a test sample. That process is not the topic of discussion here (we’ve covered it extensively elsewhere, in Potency Assay: Relative or Absolute, for example). What we want to examine is one of…
There are essentially two types of allocation we can make when forming clinical trial groups. The first is a random allocation, which, as you would expect, is unpredictable: the study group to which the subject is assigned is unknown before the process takes place. The second is formally known as deterministic, but we’re going to…
Picture the scene. You’re busily developing your assay and its data fits your chosen 4PL model beautifully. You’re just about to move onto validation—everything’s going so well!—but, out of an abundance of caution, you decide to re-run some of your data through a different software package. Disaster! It gives a different answer! Has something gone…
Relative. Thanks for reading…But wait a moment! Have you ever stopped to ask why relative potency (RP) assays are the most common potency assay? Perhaps there are cases where absolute potency (AP) would be better? Indeed, Quantics occasionally come across clients who continue to use AP for their assays. Are they incorrect? Potency is, put…
Population Pharmacokinetics (PopPK) is the gold standard method of understanding drug safety and efficacy, particularly as it provides a unique insight into the variation of drug behaviour from person to person. While not a novel methodology—Sheiner, Rosenberg and Marathe first demonstrated the analysis in 1977—techniques and approaches in PopPK have undergone continual improvement in the…
What are the most important properties of a new drug? Ultimately, we want to ensure that any new therapy treats what it is designed to treat—that it’s effective—and that the patient suffers as few negative consequences as possible while doing it—that it’s safe. These two criteria often boil down to what the drug does to…
In the first part of this series, we investigated the use of the coefficient of determination—R2—to measure goodness-of-fit. Specifically, we showed that it is an inadequate tool: a low R2 does not always mean a poor model fit; a high R2 does not always mean a good model fit; and it doesn’t even measure the…
One of the most important concepts in bioassay is that of replication. Test samples are regularly replicated 2 or 3 times. Multiple response measurements at each dose will, in general, result in a better estimate of the precision of our result. As with all biological experiments, however, there is often quite a lot of variability…
One of the most important metrics when analysing bioassay data is goodness-of-fit — how well does a dataset fit a chosen model? In development, this question can help determine which model is appropriate for a dataset, while new data will have to meet a goodness-of-fit criteria to ensure it is adequately represented by the chosen…