Clinical trials have undergone significant technological and regulatory developments during the past decade; and now, following the rise of the IoT (Internet of Things), the pharma industry is poised for even more change
Aspects of clinical trials that were once thought to be clunky and inconsistent — such as patient recruitment — have now been streamlined and made much more efficient through the use of digital technology.
In fact, all phases of drug development have been impacted by the use of innovative new technology — from protocol optimisation leveraging advanced data and analytics to enhancing patient retention through virtual clinical trials and the use of real-world data in post-approval studies.
By utilising emerging data sources and technology, the industry is capable of dramatically reducing development costs and times to market.
Digital technology has transformed how companies approach clinical development by incorporating valuable insights from multiple sources of data. Data analytics can be used to inform virtually every facet of the clinical trial.
Companies want more efficient ways to capture, aggregate and manage the right data, and analytic platforms provide a single source of operational study data that is used to guide decisions using visualisation tools.
For example, study metrics can be analysed and communicated in a dynamic way to ensure key decision makers understand them. These insights can also come from aggregated data derived from numerous studies in the same indication or therapeutic area.
Data analytics can also be adopted in the early stages of a study. In trials involving the central nervous system, the quality of psychometric assessor data is optimised by periodic audio and video surveillance. Outliers are therefore easily identified, with the opportunity to correct issues earlier during the study. This algorithm is easily applied to other studies with similar assessments.
One example of a digital technology that’s changing how clinical trials are run is electronic health records (EHRs). EHRs are real-time, digital, patient-centred records that can be created and managed by several providers.
Anonymised EHR databases permit strategic searches to evaluate and quantify the impact of protocol-specific inclusion/exclusion criteria on the patient recruitment potential of a given clinical trial protocol. They are used during protocol design and can also be informative in the event of protocol amendments that involve eligibility criteria.
An example of how this might be used would be during various computer-based cognitive assessments to monitor mental health. This data, when confirmed to be psychometrically valid, is critical in selecting patients who are appropriate for new therapies in Alzheimer’s disease and other neurodegenerative disorders. Computerised tests can be administered remotely and, in many cases, are more sensitive than paper tests.
Trial sponsors and contract research organisations have also increased the adoption of electronic informed consent and EDC (electronic data capture) systems to collect electronic patient-reported outcomes (PROs).
A PRO is any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the response by anyone else. As per the US Food and Drug Administration’s (FDA’s) guidance, clinical trials using reliable PRO instruments may be used to support medical product labelling claims.
In addition to paper-based PRO instruments being replaced by electronic versions, there has been increased use of data from wearable devices to supplant paper-based outcome measures, as well as other physician-rated instruments.
Wearables and patient sensors have been used to a large extent as monitoring devices for heart rate, sleep activity, step count, blood pressure, oxygen saturation and temperature measurements.
However, they can also expand the options for measuring endpoints in cases such as Parkinson’s disease, whereby devices incorporating accelerometry and electromyography are capable of measuring motor activity, yielding critical objective assessments of disease severity and response to therapy.
The sensors in these devices can be paired with other applications (such as those in mobile phones) to measure tremor, dyskinetic movements, gait and balance.
Other types of wearable devices can be used in a diagnostic or biomarker capacity, including assessments of walking speed in multiple sclerosis, seizure detection via wearable ECG devices and the measurement of perspiration in cystic fibrosis. The adoption of sensitive and efficient instruments can increase study power, thereby requiring fewer participants and accelerating study conduct.
Virtual (or direct-to-patient) clinical trials are emerging as a valuable development tool, as these studies use technology for patient engagement — and for the collection of safety and efficacy data — while eliminating or reducing the need for face-to-face clinic visits.
They are of particular interest in orphan diseases and in study populations with limited travel ability. Data collected in patient registries can offer insights into the natural history of diseases, and outcome-based data may be linked to other data sources such as electronic medical claims data.
As a consequence, registries enable the conversion of real-world data sources into evidence that can improve health outcomes. Another advantage of patient registries is the ability to identify new biomarkers and clinical endpoints from the repository of data. This further stimulates new research into the causes, treatment and outcomes of various conditions.
In 2012, a number of major pharmaceutical companies formed the non-profit collaboration TransCelerate BioPharma to investigate how digital technology could be leveraged to improve clinical trial efficiency.
Various initiatives have grown from this collaboration, including an industry wide information sharing and clinical data standard to drive efficiency and consistency in data collection, and to promote the interoperability and integration of EHR/EMR.
The placebo and standard of care initiative is building a collection of anonymised patient data from the control arms of hundreds of clinical trials. Reusing data from previous studies can reduce the number of patients required in a clinical trial via access to historical controls. This is of particular relevance in studies with rare disease populations, wherein the use of a placebo is infrequent, challenging or both.
In the near future, we anticipate detailed individualised biological and physiological data to be collated through the use of a combination of genomics, personalised EHRs and wearables, which will drive efficiencies in the drug development process. In addition, well planned virtual studies using endpoints acceptable to key stakeholders (patients, physicians, payers and regulators) are poised to accelerate clinical development in a cost-effective way.