Over the last 10 years alone we have seen huge advances in genomics broadly and CRISPR genome editing specifically, diagnostics, biomarkers, imaging, stem cell research, targeted cancer therapy and immunotherapy. Against this background of innovation we are now also rethinking approaches to randomised clinical trials to try and overcome the associated time, cost, patient recruitment and efficacy challenges which can often hinder our understanding about potential new investigational therapies.
Early changes in improving clinical trials have often seen the use of data science and technology as a key driver from new ethical and regulatory frameworks changing the way researchers use data to inform drug discovery and development, to new technologies, reducing the need for mass patient participation and greater targeting amongst patient populations during clinical trials. All this against an extraordinarily challenging period of global pandemic which has driven the healthcare and life sciences industries to improve patient care and dramatically speed up more inclusive and targeted drug development.
It’s probably safe to say that during the pandemic we have seen a historic accelerated and collective clinical trials process, prompting global cooperation for vaccine research and distribution for the SARS-CoV-2 virus in under a year - a pace never before possible. The success of multiple vaccines already being currently rolled out across nations, has proven it is possible to create treatments with the aid of new technologies and medical innovations. From quickly identifying the protein that affects an immune response, to scaling up the manufacturing process from tens of doses to millions - as well as being able to rapidly design clinical trials and recruit patients. Access to data and tools such as artificial intelligence (AI) and machine learning (ML) have been invaluable.
Traditionally, the average time for a new treatment to go from initial discovery to market is anywhere between ten and fifteen years, with clinical trials alone, taking up to six or seven of those and could cost on average $40,000 per patient or more depending on how rare the disease area being investigated is.
As we look to the future, conducting clinical trials through a multidisciplinary, collaborative and innovative approach will help speed up and improve the development of vaccines, drugs and other treatments.
The future is in synthetic control arms
There are many challenges associated with traditional clinical trial approaches. Factors such as speed, accuracy, costs, failure rates and diverse patient population recruitment in combination with our lack of understanding the underlying biology of diseases, hinder the evolution of the process.
Synthetic control arms could significantly contribute in transforming clinical development by helping pharmaceutical companies improve clinical trial design and success rates and specifically overcome patient stratification challenges, reduce the amount of time it takes to develop medical treatments, and improve patient recruitment by allying patient concerns about receiving a placebo and the enabling management of large diverse trials while providing the opportunity to reduce both the time and costs associated with clinical trials and gain a better understanding around the efficacy of investigational treatments.
Essentially, synthetic control arms utilise both real-world and historical clinical trial data to model patient control groups, removing the need to administer placebo treatments to patients, which can be detrimental to their health, negatively affect trial enrolment and patient outcomes. This approach can be particularly useful for rare diseases where patient populations are smaller and/or where lifespan is short due to the aggressive nature of the disease. Deploying this technology approach throughout clinical trials and taking the step to bring trials closer to the patients can hugely reduce the inconvenience of traveling to research sites and the burden of undertaking consistent medical tests. Time spent on recruiting representative patients during the trial process can also be much more efficient.
For clinicians, applying Machine Learning, and Artificial Intelligence in particular, during the clinical trial process means previously collected data sets can be analyse and at a much faster pace, often with greater efficiency and more reliability. At a time when clinical trials, and the whole health industry, is undergoing huge amounts of digital transformation, integrating synthetic control arms into medical research offers new and exciting opportunities that can revolutionise drug development.
With the number of available data sources continuing to increase – from electronic medical records, patient reported information, personal devices and health apps – these synthetic control arms could become the fastest and safest way for pharma companies to use real-world data for research into diseases with large enough patient populations.
Leveraging real-world data to drive synthetic control arms and applying AI and ML technologies to analyse it, offers the promise of more efficient, safe and fast paced trials. It poses the opportunity for researchers to quickly achieve more homogeneous patient populations and gain meaningful insights. The use of essentially a virtual patient enabling them to test which drugs are suitable for certain patient cohorts, and allows them to “fail” as many times they need in order to find the right drug for the right patient, taking a step closer to truly personalised healthcare and improved patient outcomes.
Regulation and synthetic control arms
While using patient data effectively presents the opportunity to deliver a healthier future it also raises important considerations regarding ethics, privacy and transparency. It’s critical that the industry along with other key stakeholders work with regulators to determine how patient data can be analysed to support faster medicines to more patients while maintaining trust that the way that data is used meets the highest data security and governance standards. Patients will always have ownership of their data. They need to know and have the right to know what their data is going to be used for and have a clear understanding of the positive impact this will have for research and the patient care landscape as a whole.
There is now an ongoing mindset shift towards applying this kind of advanced technology in the pharma and healthcare sector, partially triggered by the pandemic and the rapid development of the COVID-19 vaccines. With greater amounts of patient data now being collected through the use of remote monitoring technologies, electronic medical records and consumer health devices, researchers will be able to develop drugs and treatments with far more targeted, cost and time effective results.
Through using existing technology, like the algorithms developed by Sensyne, combined with close partnerships with hospitals and healthcare providers, application and access to data will revolutionise medical research, not to mention improve diversity, safety and efficiency.
The pandemic brought about an urgency to speed up the way we develop vaccines – and clinical trials were a huge part of this. We certainly wouldn’t be at almost 80% of the British population having received at least one dose of the vaccine had the clinical trials into SARS-CoV-2 not utilised new and innovative technologies, and sparked the global collaboration between pharma, technology companies and governments we all witnessed. Now, looking to the future, we must continue to innovate and make trials as quick, safe and effective as possible.
Chief of Data Analytics, Partnerships & Delivery and Commercial Director, SENSIGHT, Sensyne Health
This article originally appeared in European Pharmaceutical Manufacturer - July/August 2021