Management of the reference standard over the life of a product is critical to ensuring consistent production of material. We always want to be able to draw a link between a new batch being produced and one that has demonstrated acceptable clinical properties, particularly safety and efficacy. That’s why reference bridging is so vital: it…
So, you’re confident that you’ve got a feasible assay with a well-defined dose-response curve. But bioassays, as we all know, are fickle. The observed responses are sensitive to many factors which can lead to appreciable variability in the experiment and, in turn, a high assay failure rate. An important part of the development phase of…
Vaccines are among the most important technological developments in human history. From the earliest attempts to prevent smallpox by variolation to modern mRNA vaccines built without a virus needing to have entered the development lab, the vaccine has become a vital string in the bow of public health officials. As with all medicines, batches of…
One of the most frequent topics of discussion on this blog has been the correct and optimal utilisation of different classes of statistical models with bioassay data. Most common among these are the four- and five-parameter (4 and 5PL) models, alongside the simple linear model. There is, however, stranger at the feast: the three-parameter logistic,…
The bioassay development cycle can be a long and winding road, with vital decisions at every stage of the process. Choosing the wrong path can mean hours of wasted effort and vast amounts of unnecessary spending, while prudent choices can lead to an optimal assay, saving you time and money. That’s why we’ve teamed up…
Bioassays serve as a method of testing a product: does the latest batch of our biologic have an acceptable potency? To do this effectively, we need to ensure that our test system – the assay – continues to function as it was originally designed. This is crucial as, in the worst case, a flawed assay…
The control: a vital part of any scientific experiment. The only way to establish causality between the dependent and independent variables in an experiment is by comparison with a control. Controls are also important for establishing detection background: what does “nothing” look like on your sensor? Bioassays are no different: including control wells in your…
In bioassay analyses, there are three statistical models that are commonly fit to dose-response data: the linear model, the 4PL model, and the 5PL model. We’ve examined the properties of the 4PL model in great detail previously, and the time has come to give the 5PL model a similar treatment. The 5PL model is the…
Enzyme-Linked Immunosorbent Assays (ELISA) are a key tool for determining concentrations, forming a key component of many fields in the life sciences. From testing food products for allergens to detecting HIV in a patient’s blood and beyond, ELISA analysis is a vital component of a scientist’s arsenal. Quantics has been at the forefront of research…
When running a bioassay, we face a constant battle between two competing requirements. The assay must be able to detect when something has gone wrong with the experiment or the product, but, simultaneously, must not be so sensitive that the assay fails frequently due to the natural variability of the biology at play in a…
At the forefront of medical development lies the world of cell and gene therapy. Unlike traditional, small molecule therapeutics, treatments based on biologics can treat, cure, or even prevent genetic conditions such as sickle cell anaemia and muscular dystrophy. They can also be tailored to individual patients, giving the potential for personalised treatments for certain…
A concept we’ve discussed several times over the years is that of homogeneity of variance. We highlight it so regularly as it’s among the most critical properties that relative potency data must have before valid statistical inference can take place, and often requires careful consideration to realise. We’ve previously covered one common way to ensure…
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….