The trials of our lives

The potential of medicines has never been greater and thus
clinical trials and their design have never been more vital

What a time to be alive. Medicines that allow us go on living healthy lives for longer than ever before already exist. And we know these technologies will only get better over the rest of our lifetimes.

The last few years have seen exponential gains in our knowledge of genomics, immunology, proteomics, metabolomics, gut microbiomes, epigenetics and virology.  We’ve seen breathtaking developments in AI, computational biology and big data science.

We’ve never understood basic disease processes and pathophysiology as well as now. And technologies such as CRISPR-Cas9 are paving the way for a revolution in personalised medicine.

These are the scientific changes that will confront the world’s academics, companies, systems, clinicians and patients when devising the future of trials. But there are ethical, human and political drivers too.

Healthcare systems across the globe are getting much smarter on health inequalities – promoting inclusive, human-centred trial design.

In this article I’ll look at six areas where clinical trials will develop over the next few years: AI; trial design; RWE; regulatory; personalised medicine; and ethical considerations including patient-centricity and inclusivity.

AI is being increasingly used to streamline various aspects of clinical trials. AI tools can generate case report forms, optimise eligibility criteria and predict trial outcomes.

These advancements help in accelerating the trial process and reducing costs. AI also aids in data cleaning and anomaly detection, significantly speeding up data analysis compared to traditional methods.

These technologies can enable more efficient patient recruitment, better integration of diverse data sets and the creation of fully digital trial protocols.

It’s part of the trial landscape now – but surely, we have only begun to get the most out of this incredible toolkit.

We’re moving into a world where randomised controlled trials with one therapy for one disease, for many years the gold standard of evidence, may be only one of a number of trial types around.

For a start, RCTs face several challenges: in generating evidence in a timely manner; cost; focusing on narrow populations that hinder more widespread insights and ethical barriers.

By the time the trials have been completed and then published, RCTs can become quickly outdated. There’s also a general recruitment crisis on – in the field of cardiology alone, 30,000 RCTs have not been completed because of this.

So other methods are appearing. Umbrella trials are study designs that evaluate multiple targeted therapies for the same disease entity, such as the I-SPY breast cancer trial or the Lung-MAP – the lung cancer master protocol.

Basket trials are studies where a targeted therapy is evaluated on multiple disease types that all have the same underlying molecular aberration.

The MOT is a combination of the master interventional trial and prospective observational trial designs. It attempts to hybridise the power of biomarker-based master interventional protocols with the breadth of RWD. This approach could be well suited to collect prospective RWD across many specialties.

Then there’re decentralised clinical trials (DCTs). An example of necessity being the mother of invention, DCTs – where elements of the trial are conducted remotely - accelerated during COVID-19.

This approach increases participant accessibility and convenience, allowing for real-time data collection via mobile and web apps.

These tools can support participants in managing their responsibilities and adhering to trial protocols – ultimately improving retention and data quality.

All of these can do much to speed up the trial and improve the quality of evidence – we’re likely to see much more of this as the decade progresses.

Disclaimer alert: my company, Wilmington Healthcare, does offer RWE and RWD analysis. Nonetheless, no-one can argue that RWE won’t be a fundamental of the future trial landscape.

RWE has to be distinguished from RWD – data generated from routine, standard care of patients. RWE is generated from RWD regarding the potential use of a product.

RWE is generated by analysis, comes from pragmatic trials and observational studies, and can establish benchmarks such as the cost and outcomes of non-intervention.
RWD in itself is becoming essential to so-called synthetic control arms.

When it’s not possible to get a certain number of trial subjects together to give a robust number for a control – and in the cases of severe or terminal disease there are huge ethical considerations here anyway – a ‘synthetic’ control group can be created from RWD.

This can be obtained from electronic health records, natural history registries and patient-generated data from many sources, including wearable devices.

The regulatory environment for clinical trials is evolving, too. For example, the FDA is expected to implement laws to enhance diversity in clinical trials by requiring specific diversity markers in phase 3 studies.

Additionally, new guidelines and regulations, such as those from the International Council for Harmonisation and the European Medicines Agency, are being developed to address the complexities of modern trials, including DCT elements.

There are many elements to personalised medicine.

Our now-deep understanding of genomics is fundamental to it, but that can be applied in several ways. Targeted therapies are one: treatments designed based on an individual’s genetic make-up, such as cancer therapies that target specific genetic mutations in tumours.

There’s also pharmacogenomics – the study of how genes affect a person’s response to drugs, allowing for the prescription of medications that are most effective and least likely to cause adverse effects.

These can then be used in tandem with predictive analytics – an AI can help predict the onset of diseases before symptoms appear, enabling early intervention – and used to create a personalised treatment plan.

The discovery of biomarkers is also a huge leap. Biomarkers are biological molecules that indicate the presence or severity of a disease. The discovery of new biomarkers can lead to early diagnosis and customised treatment.

We’ve already mentioned CRISPR and gene editing, and there is the potential for personalised vaccines too.

The question is, how will this all be incorporated into clinical trials? Trials for personalised medicine will be far more complex and tailored compared to traditional trials and will need to involve the development of companion diagnostics, biomarker qualification, genomic screening and leveraging data from EHRs.

Trials will focus on populations that are more likely to benefit from the treatment based on specific genetic markers or biomarkers, and will increasingly need to be of the umbrella or basket variety.

Adaptive designs will also be needed, which allow modifications to the trial based on interim results, such as adjusting dosages or patient selection criteria in response to early data.

One really interesting development is the N-of-1 trial type. These are single-patient trials where the individual undergoes multiple treatment periods, alternating between the experimental treatment and control.

Patients serve as their own control, providing highly personalised data. This technique is potentially particularly useful for rare diseases or highly individualised treatments.

While all these developments offer staggering potential, if it is to be released, then current paradigms must be continuously challenged by all stakeholders (the new generations of scientists, physicians, the pharma industry, regulatory authorities and, most importantly, patients).

Disruptive innovation will be required: every clinical site can also be a research site, with quality checks, monitoring and research as part of the standard of care.

Indeed, the whole healthcare system could effectively become an RWE-generation system, simultaneously carrying out clinical research and clinical care.

The pandemic accelerated our knowledge of how to get a drug to a particular endpoint with the greatest speed – such as the UK government’s ‘moonshot’ and the USA’s ‘Operation Warp Speed’ to boost COVID-19 vaccine development.

Much of this was achieved by multi-agency, cross-cutting collaboration. There’s a danger that this kind of momentum will be lost – if stakeholders from different sectors, disease areas and yes, companies, fail to work together as they did then.

Perhaps every disease could benefit from a ‘moonshot’ programme, and trials can proceed in the future with clearly identified, sustainable goals to improve population health – and of course, address equity, diversity and global access to therapies.

Advances in RWE collection and future AI-based analyses of all data will provide deep evidence, which will be needed to realise the goal of perhaps the ultimate technology in play here, personalised medicine.

If all of these factors synchronise, the future could offer the ultimate answer to the pharma’s great question – how do you get the right treatments to the right patients at the right time?

Oli Hudson is Content Director at Wilmington Healthcare.
Go to wilmingtonhealthcare.com