Precision medicine: what barriers remain?   

The importance of the individual has been widely established in medicine since time immemorial. The well-worn adage that physicians should “treat the patient, not the disease” has been around since the 19^th century, and the awareness of that message is far older than that. Even Hippocrates, the ‘father of Western medicine’ who treated patients in the fifth century BC, stressed the importance of treating each patient as an individual.

“For the sweet [medicines] do not benefit everyone, nor do the astringent ones, nor are all patients able to drink the same things,” Hippocrates wrote.

Hippocrates might have tailored his rudimentary treatments based on the patient’s age, physique and other easily observable factors, but personalised medicine in the 21^st century offers the promise of therapies customised based on the study of what truly makes us unique: our DNA.

Advancements in genomics, proteomics, data analysis and other fields – both medical and technical – are gradually facilitating the development of laser-focused drugs, as well as the ability to predict people’s personal risk factors for particular diseases and how individual responses to various treatments might differ.

After years of anticipation, there is now evidence that governments around the world have clocked the importance of personalised medicine and are driving efforts to the build the genetic data sets and biobanks that are required to push the science forward. Former US President Barack Obama launched the Precision Medicine Initiative to great fanfare in 2015; the scheme has since evolved into the All of Us research programme, which aims to gather health data from more than a million US volunteer-citizens to unlock new insights.

In the UK, the 100,000 Genomes Project reached its goal of sequencing 100,000 whole genomes from 85,000 NHS patients with cancer or rare diseases. Genomics England has noted that so far, analysis of this data has revealed “actionable findings” in around one in four rare disease patients, while about 50% of cancer cases suggest the potential for a therapy or clinical trial.

“You can match a blood transfusion to a blood type – that was an important discovery,” said Obama at the launch of the Precision Medicines Initiative, summarising the broad appeal of personalised therapies and diagnostics. “What if matching a cancer cure to our genetic code was just as easy, just as standard? What if figuring out the right dose of medicine was as simple as taking our temperature?”

The stage might be set for personalised healthcare to dramatically transform public health, but few in the medical field would deny that the world is hardly ready yet. Transitioning from the traditional one-cure-fits-all treatment model to new processes that leverage patients’ genetics, lifestyles and environmental risk factors is an immense task that presents challenges in both the laboratory and the clinic.

Oncology is, by a landslide, the field that has been most impacted by developments in precision medicine; around 90% of the top-marketed precision treatments approved in 2018 were cancer therapies, while other therapeutic areas have lagged far behind. The majority of approved precision medicines in oncology achieve something of a halfway house between the old way and the new – they fall short of being tailored to a specific individual, but they allow for more detailed stratification of patients by the oncogenic mutations of their tumours, which may be driving cancer cell survival and growth.

Common examples of these mutations are HER-2 in certain breast and stomach cancers, BRAF in melanoma and EGFR in lung cancer. High expression of these proteins at cancer sites can be targeted by precision treatments, such as Roche’s monoclonal antibody Herceptin (trastuzumab) for HER-2, Genentech’s BRAF inhibitor Zelboraf (vemurafenib), and Roche’s EGFR inhibitor Tagrisso (osimertinib). Regulators such as the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are also increasingly approving ‘tumour-agnostic’ treatments – the first and most famous of which is Merck’s immunotherapy Keytruda (pembrolizumab) – which target specific biomarkers regardless of tumour location.

But despite the availability of a growing menu of personalised cancer treatments, actually matching patients up to the right therapy can be difficult. According to a survey of US acute care organisations conducted by Definitive Healthcare and published in December 2019, just over 20% had established precision medicine programmes. Investment in genomic testing is vital to quickly get patients on the best treatment course, but financial and operational barriers remain.

The foremost among these is the cost associated with genomic sequencing and the use of companion diagnostic devices, cited by 28% of Definitive Healthcare’s respondents as the biggest challenge for already-established precision medicine schemes. Lack of expertise is another obstacle, as many physicians may struggle to accurately interpret test results without specialist assistance – another major cost driver for clinics and hospital departments trying to build pathology teams that are up-to-date with the newest tests. A 2018 survey of 160 oncologists by Cardinal Health found that 60% of physicians who don’t use genomic tests avoid them because of the difficulty of interpreting the data.

In clinical research and development, too, there are growing pains associated with moving the pharmaceutical pipeline towards drugs targeting smaller patient sub-groups. Again, cost is a central issue – companion diagnostics don’t come cheap, finding and validating biomarkers to guide targeted therapies is a lengthy task, and analysing vast amounts of data often requires new teams with specialised knowledge.

The expense of incorporating a host of new processes into innovative trial designs – not to mention the cost of manufacturing cell and gene therapies – obviously has an impact on the list price of personalised drugs that win approval. This is most clearly seen in the eye-watering prices of some of the world’s first truly individualised cancer treatments, chimeric antigen receptor T-cell (CAR-T) therapies.

Treatments such as Novartis’s Kymriah and Gilead’s Yescarta remove T-cells from the patient’s blood, modify them to target tumour cell antigens and then infuse them back into the blood stream. These therapies have achieved impressive results in rare and advanced cancers, but cost upwards of $400,000 per patient, limiting their reimbursement options among both private and public payers. Promising advances in CAR-T manufacturing and potential ‘off-the-shelf’ T-cell production could help bring these costs down in the years to come, but for now the problem remains.

As for the broader clinical trial eco-system, these studies have been historically set up to assess a drug candidate’s safety and efficacy in an increasingly large segment of the patient population, building evidence towards the regulatory approval process. Bringing a personalised medicine through the clinical development process is a new paradigm in a number of ways; as well as the aforementioned cost drivers, there can be an extra enrolment burden to identify and recruit patients – this is already a common cause of trial failure, but it’s all the more difficult when you’re looking to access a small patient sub-group with the appropriate biological profile.

The difficulty of providing sufficient evidence of safety and efficacy can also present issues where current regulations struggle to accommodate new innovations in personalised medicine. Smaller trial designs present statistical problems in terms of understanding a drug’s definitive risk-benefit profile, and while some ‘personalised’ applications can be discovered as part of larger trials that fail to meet their endpoints outside of a select patient group with particular biomarkers, many current regulations don’t accept post hoc analysis and would require an entirely new trial.

“Personalised medicine developers desire better guidance on how best to design a successful clinical trial for a personalised therapy, because absent guidance, they risk presenting suboptimal evidence regarding stratification options,” reads a 2017 study on personalised medicine barriers, published in the Journal of Law and Biosciences. “Designing clinical trials for differently responding subgroups (for example, biomarker-positive and biomarker-negative groups) requires additional time and resources. Companies are reluctant to make this investment without a commensurate increase in the certainty of regulatory approval.”

The increasing use of surrogate endpoints, conditional approvals and real-world data is helping to address these issues, but they’re not yet an ideal solution. Conditional approvals rely on very careful post-marketing observation and analysis, while the value of surrogate endpoints has been questioned, adding to the tension between accelerating approvals and ensuring patient safety.

The ultimate benefits of creating more personalised treatments are clear, and their advantages for human health could, in the long-term, be matched by their economic returns. After all, quickly treating patients with the right therapy for them – or, even better, using knowledge of a patient’s genetic risk profile to prevent illness in the first place – would be a huge financial gain for overburdened health systems.

Today’s costs are gradually falling, as NIH data on DNA sequencing costs demonstrate. But there is still a long way to go before we can wave goodbye to the blanket drug development that has dominated modern pharma for decades, even in the advanced field of oncology, let alone other therapeutic areas. Only a sustained and holistic push – from regulators, drug developers, clinicians, governments and others – will be enough to bring us over the line.