AN AGILE APPROACH
adapt and redesign phases of a project. When it comes to clinical trials, agility can be incorporated into both the design and execution stages in order to refine and improve the process. Incrementally optimising a clinical trial like this increases its efficiency, meaning we can help more patients more quickly.
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Agile design Agile design takes a complex project and breaks it down into relatively small components, fine-tuning each one for the benefit of the overall project. In clinical trials, numerous critical components can be optimised. The clinical endpoint is a measurement of some symptom or reading which is the target outcome of the trial – for example, the trial may aim to reduce the severity of a certain symptom in subjects. This endpoint should be established in the trial design, along with the parameters of the trial and best practice procedures, which should be aligned across all test sites to avoid costly inconsistencies. Operations should be governed by a template protocol, and efficiency is maximised when the trial developers draw on previous experience, leveraging the perspective and expertise of the contract research organisation (CRO), the sponsor and the sites. Refining each of these details individually improves the overall trial. Operational agility can be built in from the start. The goal of a clinical trial is to identify the right drug and the right dose to have the right effect, within constraints including safety, price and time. In a classic design, pre-clinical/comparative studies will suggest the end point, safety considerations, dose range and therapy windows. An agile clinical trial design builds in opportunities
to make alterations as the trial proceeds, in response to observations. For example, sample size may be flexible by allowing for “unblinded sample size re-estimation”, meaning that changes can be made easily during the execution of the trial, based on interim results. Another common process is dose escalation, beginning with a low dose in a small population and gradually escalating to higher doses until an adverse event (AE) incident or safety requirement is failed. The aim of this is to identify patterns and always pursue the endpoint.
y definition, agility is the ability to move quickly and easily. In project management terms, it means being able to frequently
Agile design anticipates and looks out for differential responses in trial data to identify patterns by considering a wide “net” of observations. This net can include comments from site staff, patients, diaries, online and secondary comments. An agile trial can take all this data into account.
Agile execution Agile execution looks out for opportunities where adjustments could improve the process as it progresses. A key application of agility is the identification of sub-components of a larger system – this means picking out important details from a much bigger picture. Clinical trials generate huge amounts of data. Each subject can be characterised by an array of factors, including age and gender. On top of this, more subject data is gathered. Basic vital sign readings can include pulse, blood pressure (BP), weight and height, and a blood chemistry panel can give 12 measurements. The trial may study 10 symptoms, graded from mild to severe, which are measured over the course of subject screening, enrolment, interim visits, end-of-therapy and post-therapy stages of the trial. More relevant data includes dose levels, enrolment profile, site information, predisposition and more. The amount of data is huge, and we still can’t know whether we’ve understood all the important parameters. In a clinical trial, a subset of this data is tested to see if there are any useful correlations between any subject characteristic and an outcome parameter. The important outcome parameters of a study are the frequency or severity of any AEs and the favourable value of the endpoint – that is, the effects, positive and negative, on the subject. These factors are usually correlated with dose and sometimes with therapeutic protocol parameters, such as duration. Agile trial execution allows us to identify whether some other parameter correlates with an outcome parameter. This parameter could be something as specific as blood potassium levels. If this parameter correlates with an outcome parameter, this may warrant further study. Identifying this kind of statistically differentiable outcome requires analysing the process as it is executed. For example, perhaps favourable outcomes seem to be more strongly correlated with creatinine clearance values, especially in 20- to 40-year-old males. When this is
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