Personalised healthcare – a new age in drug development

Personalised healthcare (PHC), a tailored approach to treatment based on the molecular analysis of genes, proteins and metabolites, has been a hot topic for many years, having been heralded by early successes with drugs like Herceptin for breast cancer (Roche, 1998) and Xalkori for non-small cell lung cancer (Pfizer, 2012). Scientists now have a greater understanding of disease heterogeneity; we know that breast cancer is not one, but many different diseases, and that HER2-positive breast cancer is an aggressive form caused by the overly active HER2 protein in cells.

While oncology is by far the most advanced area of PHC, breakthroughs in other areas, such as immunology, central nervous system, infectious and cardiovascular diseases have also been seen. We believe that PHC is now poised to become a mainstream activity as four big trends converge:

  1. Advances in genomic science and next-generation gene sequencing, resulting in a better understanding of disease pathways and heterogeneity. We can now identify many genetic mutations and their associated diseases at a much lower cost and with greater speed.
  2. Increasingly sophisticated diagnostics for screening and risk identification, as well as drug toxicity/safety profiling. Diagnostic tests and biomarker information are key components of PHC, as they are used to identify and predict how well a patient will respond to a particular medication. This enables physicians to determine the best dosage and treatment regimen. Tests can also identify patients who will not benefit from a treatment, thus reducing drug wastage,[1] non-compliance (which leads to adverse health effects, especially for chronic conditions), and healthcare costs. Pharmaceutical companies will be able to identify and eliminate that ‘one in a million’ serious adverse event that can knock an approved product off the market.
  3. Convergence of digital technology and healthcare, creating greater precision and transparency in modern medicine. Many patients are actively using health apps to measure basic functions such as heart rate and physical activity. Similar technologies have the potential to collect more sophisticated clinical information that could help patients and physicians manage diseases like diabetes or assist pharma in running clinical trials.
  4. Cost containment pressures within the healthcare system, forcing regulators and payers to demand that all novel treatments are cost-effective. PHC promises optimized use of resources in healthcare through better compliance and fewer unnecessary treatments/side effects and associated costs.


Implications for life sciences companies


Literature shows that one third of FDA drug approvals in 2013 were already linked to a companion diagnostic approach and ca. 600 industry-sponsored trials with a companion diagnostic element are currently taking place. This is testament to the fact that targeted drugs can, in theory, command premium prices through superior clinical outcomes and a faster time to market.

However, PHC has significant implications for the pharma business model; while new drug stratification regimes will reduce the market size of some drugs, biomarker testing will identify new patients for other drugs (e.g. gefitinib was originally prescribed for non-small cell lung cancer, but has now been shown to be effective against many cancers). This approach has the potential to improve sales and profits through a new business model, yielding differentiated products for segmented populations.


Key challenges in personalised medicine


Not surprisingly, this new model presents challenges for the traditional pharma model, both commercially and in R&D. R&D teams must now:

  • Understand disease pathways for complex conditions such as cancer and dementia;
  • Identify the correct patient cohort, obtaining the right tissue samples and accurately monitoring these samples throughout the clinical trial duration; and
  • Understand how to use advanced analytics for data analysis so that population-wide disease patterns and trends can be determined and utilized in personalised treatments.

Commercially, teams have to:

  • Understand the regulatory and reimbursement environment, which is complex, poorly defined for drugs versus diagnostics, and lacking in standardization across geographies; and
  • Educate physicians on new technologies, approaches and therapies to adopt and remain abreast of these as they continue to advance.


Harnessing potential


Many are already taking significant steps to address these challenges and harness the potential of PHC.

Diuretics, a UK-based research firm, analysed the PHC efforts of numerous pharmaceutical companies to determine which ones are best positioned for the new biomarker-driven world. It is evident that the industry is very active in this area, but it is less clear who will succeed in the long term.

FiercePharmaMarketing [1] reported the results of Diuretics’ study. The key industry players and their latest achievements are summarized below:

Roche has been very active in developing medicines specifically for the patient subgroup with a high amount of HER2. Their treatments target HER2, kill these cancer cells and decrease the risk of recurrence. Roche has the advantages of an integrated diagnostics division and a strong pipeline; approximately 60% of their late-stage compounds are being co-developed with a companion diagnostic test to help personalise treatment. In 2015, Roche acquired a majority stake in Cambridge, MA-based Foundation Medicine (FMI), which provides genomic analysis to pair cancer patients with appropriate treatments and trials.

Novartis has a molecular diagnostics unit integrated within the Novartis Pharma Division, which focuses on innovative companion diagnostics and biomarker tests. So, while many international pharmaceutical companies are working to bring companion diagnostics to the market alongside targeted therapies, Novartis aspires to be a world leader in molecular diagnostics, which is a key element of PHC.

AstraZeneca (AZ) is now a major contender in this space, despite having started out as a ‘dark horse’. Having successfully fended off Pfizer’s advances, AZ is delivering on its claims of a strong product pipeline. In November 2015, the FDA approved Tagrisso, a new lung cancer treatment, in record time. Tagrisso is aimed at a subset of lung cancer patients whose tumours have spread and developed a treatment-resistant mutation (AZ worked with Roche Diagnostics to develop the test for the mutant genes). This represents a very small but well-defined group of patients, which helped AZ speed up clinical trial completion.

Tagrisso is the second targeted oncology drug launched by AZ with a companion diagnostic in less than one year following the approval of Lynparza for ovarian cancer.

PHC is key to AZ’s turn-around efforts, as many of its blockbusters approach LOE (e.g. Nexium, Crestor). Half of the drugs they expect to launch by 2020 will come from companion diagnostics and 80% of the current drugs in its R&D pipeline are personalised.

AZ recently acquired (for $150 million) Definiens, a German company specializing in tissue-based biomarker tests. AZ does not intend to build a diagnostics business in house to match that of Roche, but will ‘cherry pick’ different ‘best in class’ diagnostic partners for different drugs. Many may be surprised that AZ is so well advanced in this space.

The remaining companies are best described as ‘ followers‘ – they have demonstrated solid work in PHC, but are less prepared for a transformational shift.

Pfizer is focusing on targeted lung cancer treatments for UK patients through its partnership with Cancer Research UK and AZ.

GlaxoSmithKline has partnered with GE Healthcare to improve treatment for metastatic melanoma, the most deadly form of skin cancer. Clarient Diagnostic Services, an affiliate of GE Healthcare, will support diagnostic laboratories to develop a standardized and uniform approach to testing cancer patients. This is very important, as there is currently no standard in diagnostic testing.

Merck secured approval for Keytruda to treat non-small cell lung cancer in October 2015. The drug treats only 20% of the patient population, which is a very small market. However, its cost effectiveness has earned it a huge $12,500/month price tag.

Shire has recently acquired Baxalta to consolidate its position in the orphan drugs market across multiple therapeutic segments, including gastroenterology. Orphan drug clinical trials are now adopting genetic biomarkers and clinical endpoints, which is a major step towards PHC.[2]

PHC is a rapidly evolving space. In forthcoming articles, we will look more closely at the clinical and commercial challenges and debate and discuss the potential solutions facing the various stakeholders.

For more information on how to harness the benefits of PHC at your organisation, contact us at a-connect.


[1] FiercePharmaMarketing, November 26, 2014, “Who are the stars of personalised medicine?”


[1] Many of today’s available drugs are effective in only 30–60% of treated individuals.

[2] The orphan drugs market is very attractive, being worth $100+ billion and having a CAGR of 12%, which is almost twice that of the general drugs market. Of the top 10 projected best-selling drugs worldwide in 2015, almost seven had orphan status. The regulatory environment is more favourable and 2014 was a record year for FDA/EU/Japan orphan designations.

Transitioning to Personalised Healthcare: Understanding the key scientific and practical challenges

In the previous article, we discussed four key trends shaping the Personalised Healthcare (PHC) model and how the profile of a patient’s genetic variation could be used to tailor drug treatments.

We already know that many conditions such as heart disease, cancer and Alzheimer’s are heterogeneous diseases that come in several clinical and histological forms. They are caused by a combination of genetic, lifestyle and environmental factors.

These complex diseases are often chronic and impose a huge cost burden on our healthcare system. While PHC, more accurately known as precision medicine (PMx), aspires to provide tools to better manage them, the scientific and practical hurdles that researchers, healthcare professionals and patients must overcome to “get there” is no mean feat. For many pharmaceutical companies, PMx means changing established practices in all aspects of the business—from the earliest stages of target identification and drug discovery, through to clinical development, regulatory approval, commercial development and operations, and sales and marketing.

However, many experts believe that we have now “reached a critical time to invest in innovation due to new technologies reaching an intersection of speed and precision and our unprecedented ability to target incurable diseases” [1]. This has been exemplified by the US National Institute for Health’s $2-billion budget increase for 2016—a sign of the growing importance of R&D [1].

This article will focus on some of the R&D challenges as organisations transition from the traditional blockbuster business model to the newer, PMx-based approach.

Finding the key factors of disease progression

At the earliest stage of drug discovery, scientists must identify genetically based drivers of the disease and determine “which target molecules to test for”. Consider cancer: solid tumours may contain tens, even hundreds of different genetic mutations. However, only some of these driver mutations are actually responsible for tumour growth and disease progression. The others, secondary mutations, are not directly associated with disease progression, but are abnormalities that accumulate with tumour growth. Scientists are interested in finding the drivers and then developing drugs that block these targets. Genomic tests can then identify and treat patients with those driver mutations.

However, at the moment, PMx is not quite as precise as that. Cancer cells have many different mutations; to decipher driver mutations from secondary mutations on a patient-by-patient basis is very complex. So far, scientists have only identified about 50 genetic mutations known to drive cancer [2]. There are thought to be hundreds of unidentified mutations, though. Consequently, genetic tests of tumours incorrectly show that there is no mutation known to cause the cancer. Worse still, even if a cancer-driving mutation is found, doctors cannot treat it because a drug does not exist or may only be approved for a different kind of cancer.

Although there are hundreds of experimental drugs in clinical trials that are designed to target specific driver mutations, we cannot be totally sure that the experimental treatment in question will even work in a particular patient. There are success cases like Vemurafenib, designed to target cancers linked to the faulty BRAF gene, but in other cases, such as BRAF mutant bowel cancer, treatments may only work in the laboratory or in only a minority of patients.

Therefore, different genetic mutations offer different levels of certainty when offering targeted therapy, indicating the need for an internationally agreed categorisation system. It would be helpful to know that, for a given genetic mutation, sufficient evidence exists that a specific experimental treatment will work and therefore merits clinical trials. This would save precious time, resources and money.

Biomarkers and Companion Diagnostics

However, before clinical trials can even commence, scientists must discover and develop biomarkers early in the drug discovery process (ideally at lead identification stage) and effectively translate pre-clinical models into clinical ones. The development of biomarkers to stratify diseases into better-understood, molecularly characterised subtypes requires access to well-defined patient populations and biological samples, together with longitudinal clinical data and medical and treatment histories.

Nowadays, scientists are trying to develop new drugs concurrently with biomarkers and Companion Diagnostics (CDx). Historically, CDx was a very different business from pharmaceuticals, with far lower profit margins, rendering it unattractive to drug companies. Diagnostic tests were only being used after drug development to rescue or increase drug value.

Under the current R&D model, researchers identify target patient subgroups using genetic tests and biomarkers in advance and then design drugs for these specific populations*.

The pairing of diagnosis and therapy will deliver improved R&D productivity, since the patient subgroups that will benefit from the drug will have been pre-selected before clinical trials. We would expect a better average response rate for drugs, currently 50% across all categories and just 22% in oncology [2]. This improvement, in cancer alone, would more than compensate for the cost of diagnostic technology, as shown in the case study below.

Changes to the clinical trial process

The clinical trial process is also changing to accommodate the new PMx era. In the past, clinical trials of cancer drugs were conducted by grouping patients according to cancer location, e.g. breast, lung or colon. Today, we know that cancers with the same driver mutation(s), irrespective of where they are in the body, share commonalities. Therefore, treatments are now being conducted on patients with the same cancerous mutations in different parts of the body. This means physicians need to take account of both the location and the mutation when selecting a treatment.

New types of clinical trials, such as adaptive clinical trials, have also been developed to reduce trial cost and shorten drug approval times. Adaptive clinical trials allow researchers to modify study parameters (e.g. dosing, sample size and schedule) according to accumulated data at prospective interim time points. This provides the best clinical outcomes.

Although the benefits of a more flexible, adaptive clinical trial are obvious, there are also practical challenges; for example, the number of treatment doses in the adaptive dose-finding stage can be significantly more than traditional clinical trials. This scenario, with its potential treatment variability and immediate response to the adaptive changes, can inherently increase the quantities of study drug needed.

As PMx advances in the oncology space, one of the most significant challenges arising is the lack of high-volume tissue samples. It is important to study tissue samples as a starting point for disease diagnosis, but also to understand what changes occur in the patient’s tissues during the course of a treatment. Scientists need to analyse individual’s tissue samples before, during and after treatment in order to plot treatment response and reaction variations across patients with the same disease type. This requires multiple samples from a given patient (which are large enough to understand the disease) and samples from many patients. Therefore, a better access protocol to well-characterised tissue samples of high quality is paramount.

Next steps and combination therapy

In oncology, patients are more likely to benefit from combination therapy—the treatment of multiple mutations simultaneously in order to stop tumour progression from the start of treatment. Ideally, if we know that a patient will benefit from combination therapy as opposed to monotherapy, the patient can avoid trying out various drugs unsuccessfully while the cancer continues to progress. However, identifying and validating combination drug therapies is very complex and the current clinical trial process is not set up for such testing, but rather for studying drugs individually and in comparison. Therefore, the current regulatory and drug approval system must adapt to support simultaneous testing of multiple drugs.

Finally, PMx is only possible because of the speed with which we can now sequence the human genome. The plethora of data generated from genetic tests and clinical trials requires sophisticated technology platforms to store and intelligently analyse the data to draw meaningful real world conclusions.

Education is also critical. Most physicians are unable to interpret genetic test results and, therefore, do not request that patients have these conducted in the first place. Patients need to understand how genes play a role in disease and the benefits of genetic testing. Pharmaceutical companies, payers and insurance companies need to acknowledge the importance of biomarkers and diagnostics so that they can support discovery of treatments that work.

In the next article, we will focus on the business challenges associated with PMx, in particular, the changes in the regulatory and reimbursement landscape to enable advancement of PMx.

Case study: PMx creates value

Better targeting allows physicians to begin effective treatment earlier while avoiding costly treatments that offer little value, and often come with side effects.

Today’s targeted approach to cancer care is redefining traditional treatment patterns; we no longer want to do everything that might help a patient, but rather, we should do exactly what the patient needs, based on the unique molecular profile of his/her disease.

By pairing diagnostics with specific drugs, pharma companies can offer more value to all parties; patients receive the “correct” treatment, hospital costs are reduced, clinical trials show greater success, and the reduction in adverse events decreases nasty lawsuits. The potential savings to the system are huge.

Consider Herceptin: the drug costs $79,181 per patient when a diagnostic is not used. But when it is used only with patients who will respond, the cost drops to $53,738—a difference of $25,443. Consider all the drugs that failed approval after clinical trials because they did not show efficacy in enough patients. If it were possible to find the gene expression and molecular pathway of the subgroup of patients that responded, those “failed” drugs could potentially be resurrected and even save many lives. This approach of testing for precision with diagnostics costs more initially—which is probably why it is not already being used widely. But pharma companies need to stop thinking about the short term, and begin considering how they can better pair a diagnostic with a drug in order to succeed in the long term.


[1] Joe Jimenez, CEO, Novartis, 22 March 2016, “A Critical Time to Invest in Innovation”.
[2] Popular Science, 27 August 2015, “Everything You Need to Know About Precision Medicine”.

 *This process has been held back somewhat by cost; the first human genome sequence cost $3 billion and took many years to completely map. Today, costs are decreasing and the effectiveness is rising exponentially. Several companies offer personal genome sequencing for a few thousand dollars or less; within 10 years, there is an expectation that sequencing a personal genome will take only an hour and cost a few hundred dollars, or less than an MRI.



Seizing the Opportunity: Developing Personalized Medicine Technologies

Traditional medications have long struggled to deliver a desirable level of effectiveness for patients. The Personalized Medicine Coalition suggests that efficacy rates for drugs can vary from 25% to 60%. Drug compliance is also a major concern – a number of studies have shown that compliance is often only around 50%, a figure in part driven by negative side effects. While drug treatments often deliver significant improvements in quality of life, many patients continue to struggle with the compromises that come with taking a drug designed to be one-size-fits-all.

To address these deficiencies, medical technology is turning to personalized medicine: medications designed to work for the individual. The US federal government has shown recent support for technological innovations in healthcare, as demonstrated by its commitment to the 21st Century Cures Act, the Precision Medicine Initiative and the Cancer Moonshot.

About a quarter of novel drugs approved from 2014 to 2017 were personalized medicines, which has provided unique opportunities for innovative therapeutic and diagnostic companies. There are now 32 approved companion diagnostics (not including other FDA-cleared, pharmacogenetic assays) that can identify individual differences in drug metabolism and pave the way forward for personalized medical interventions.

As shown by results over the past half-dozen years, sorting patients based on their genome using evolving technologies (such as DNA sequencing and other biomarkers, as well as patient demographics/history) has the potential to overcome a number of problems – including efficacy and safety concerns. These technologies may also reduce unnecessary treatments and prevent spiraling treatment costs, as well as save on R&D expenses for developers and manufacturers.

Acknowledging the challenge of personalizing medicine

While many believe that personalized medicine (along with the companion diagnostics to implement it) will be the best way to achieve better and cheaper healthcare, there are still a number of (real or perceived) barriers (see Figure 1). These barriers drive drug research and development back towards the one-size-fits-all approach, rather than encourage the development of personalizable treatments. For companies already investing heavily in traditional drug development approaches, it may be difficult to imagine taking on these new challenges.

The dynamics of personalized drugs and companion diagnostics

Figure 1: The dynamics of personalized drugs and companion diagnostics

The Tufts Center for the Study of Drug Development estimates that the R&D cost for developing a new drug is between $1.4 billion and $2.9 billion – a huge investment for any organization. In addition, Health Affairs has highlighted the growth of ‘step therapy’ (trying one lower-cost drug before another more expensive drug, also sometimes called ‘fail first’) – an approach that indirectly reins in innovation and encourages the status quo. Step therapy grew to 73% in 2013 (up from 27% in 2005) among employer-sponsored healthcare plans, despite studies showing that, in a number of cases, delaying treatment can increase mortality or worsen other outcomes. So, how can medical technology businesses overcome these challenges to achieve the promise of personalized medicine?

Targeting future research efforts and collaborating strategically for success

Medical technology companies need to focus their energies on developing and utilizing tools that can match patients to the right drug (or other intervention) at the right time. To do this, they need to harness the potential of diagnostic and prognostic tools and resources, such as genome sequencing, novel protein and metabolic biomarkers, and, in the future, machine learning and artificial intelligence. They also need to identify the next research capacity bottleneck (rather than chasing their current competitors) so that they can lead the way with these evolving technologies.

Targeted investment in developing technologies, such as genome-based therapeutic technologies, may help with screening for compounds that have broader uses for targets with low genetic variation. Using state-of-the-art tools to target novel biomarkers may also be pivotal to success (to find out more about novel biomarkers, take a look at my White Paper on the topic here). Other valuable investments might explore blood-testing technologies to replace invasive biopsies (so-called liquid biopsies). Accumulating data on other biomarker types, such as immunoassays and metabolomics, could also provide an invaluable data bank for future research.

Medical technology companies will need to work with pharma and biotech firms early to identify, validate and gain approval for companion diagnostics. In addition, identifying companion diagnostics early in the drug development process may prove useful for selecting patients during clinical studies and getting timely co-approval during the regulatory process. Ultimately, creating the infrastructure for efficient trials will enable organizations to adjust their approach and work towards nichebusters rather than blockbusters.

Finally, medical technology companies will also need to develop the conversation with medical device companies to identify additional areas where a personalized approach may be effective. Medical device trials could also benefit from better patient selection. By pursuing complex health economic analyses in partnership with a range of therapeutic, diagnostic and medical device firms, as well as potential competitors, medical technology companies can drive and take charge of innovations in personalized medicine.

The application of personalized medicine may not only improve patient outcomes and lower healthcare costs, but also provide valuable business opportunities for perceptive medical technology firms. Organizations utilizing machine learning, artificial intelligence and other big-data technology can also contribute to personalized medicine, and benefit commercially as well. If your organization is looking to position itself at the forefront of developing medical technologies, contact a-connect today to find out how we can support your work.