01 Jun 2018
When the human genome was successfully sequenced it was thought to represent the ultimate cracking of the human genetic code. But it has only served to increase our awareness of how little we know.
Personalised medicine is the application of genetic code and molecular data analysis to understand disease processes at the most basic level; this then can be used to engineer a targeted therapeutic drug to maximise its chances of success while minimising side effects.
We see a world where personalised medicine goes beyond treatment of diseaseThe same techniques can be used in drug discovery, and in clinical testing of patients to select those more likely to respond to particular treatments and highlight those with a predisposition to a particular disease.
We see a world where personalised medicine goes beyond treatment of disease; from disease prevention to diagnosis, clinical trial design, disease monitoring, treatment monitoring and prognostic indication. This will spawn a new paradigm in healthcare that uses advanced technologies, involving patients in the management of their own health to prevent and treat diseases effectively and efficiently while also promoting healthy living.
Targeted treatments are already in use. Lung cancer, one may think, is a single disease; far from it, it is classified as small cell lung cancer and non-small cell lung cancer, with the latter further split into a myriad of types depending on the genetic mutation causing the cancer.
It is thought that 13 known mutations may account for around half of non-small cell lung cancer cases with the mutations causing the other half remaining unknown. A drug that works for some forms of a disease may not work for others but in some cases companion diagnostic tests are available that help determine whether the drug is likely to work.
If a patient screens positively on the diagnostic test for a marker that is specific for a type of disease or genetic mutation, then the medicine will be prescribed, otherwise alternative treatments will be explored. Examples of drugs that are used in this way are AstraZeneca’s Iressa for certain types of lung cancer and Roche’s Herceptin for breast cancer.
PriceWaterhouseCoopers, an accountancy firm, estimates that patient response rates to medicines can be very variable ranging from 20% to 75% depending on the drug; that suggests there is room for significant improvement in drug targeting to the benefit of patients and the healthcare system.
Screening for potential side effects could also take on a whole new dimension. NG Pharma America, a consultancy, estimates that about 100,000 people in the US die from adverse reactions to medicines and more than 2 million are hospitalised every year because of side effects – this is no small matter.
The situation could be improved by better understanding of how a group of enzymes, collectively known as cytochrome p450, act.
Jimmy Muchechetere, Investec Wealth & Investment’s Healthcare Analyst, discusses personalised medicine and the need for healthcare and technology to combine to provide a powerful platform for disease surveillance, diagnostics and treatment.
This means that the effect of medicines can vary from one patient to another; for example, if the medicines are broken down too quickly they will not have enough time to work and so some people require bigger doses or longer treatment periods. There is, however, a new cytochrome p450 test that can help effect optimal dosing and treatment intervals in every person, based on their individual metabolism.
Personalised medicine could be closer to home than one might think. In 2017, Babylon App, a smartphone application, took the medical world by storm by showcasing its technology that it uses to monitor and advise on the health of the phone owner. It employs a team of more than 100 artificial intelligence researchers and medical professionals building a database of diseases and creating the world’s largest repository of medical knowledge in the hope its technology – in collaboration with doctors it employs – can triage, diagnose and advise via a person’s mobile device.
This is just one example; in 2016 the US Food and Drug Administration approved 36 connected health applications and devices ranging from lung-function devices to artificial kidneys and smart heart pacemakers.
Ease of use could be a driver of rapid adoption although leaving one’s health to a new digital tool or algorithm where one is unsure how rigorous its testing has been or whether it has been peer-reviewed might give some pause for thought.
In the near future, people will probably be able to get data-driven triage and preliminary diagnoses that are highly specific to them, thus removing the need to clog up GP surgeries and NHS hospitals for minor ailments while also probably reducing medical errors; a British Medical Journal study in 2016 ranked medical errors as the third leading cause of death in the US and the killer of about 1,000 people a month in the UK.
Complexity of treatments will undoubtedly increase. Doctors’ workloads will probably rise with an increase in patient data generated. Nevertheless, treatment periods should not necessarily rise as data analytics and algorithms have improved materially since the early 2000s when the first human genome was sequenced.
A step-by-step algorithm that ‘decides’ the best medicine for each patient once the relevant tests have been taken and interpreted should in theory free up a doctor’s time to concentrate more on the nuances of treatment (think socio-economic consequences, environmental sources of the disease or impact of spreading the disease) and more complex disease processes.
Additionally, data analytics can reveal patterns that may help identify those at risk, potentially allowing for preventive treatment. Finally, gene sequencing is getting faster and cheaper; the vision is for it to be a bedside test costing less than $1,000 and delivering results within hours while being highly specific and thus vastly improving treatment times and outcomes.
“If it were not for the great variability among individuals, medicine might as well be a science and not an art”
Sir William Osler 1892
The dramatic increase in data generated will bring its own problems. Privacy, security, health insurance and legal ownership of the data may prove to be minefields that need to be negotiated with extreme caution. Cloud-based data stores can be hacked while physical storage devices are cumbersome, can fill up quickly and can be corrupted or worse lost.
Another potential source of caution will be where the liability lies in cases where a machine-learning algorithm, feeding off data analysis, makes a fatal error in diagnosis or advice. Finally, if people employ the ‘right to be forgotten’, taking their data out of the population data set, how will that affect the robustness of that data and perhaps the veracity of conclusions drawn from it?
The positive economics of personalised medicine are partially negated by diseconomies of scale. One of the advantages of mass market treatments is that they can be produced in volume and the incremental unit cost is ever falling until efficiencies are maximised.
By tailoring medicines to each individual and for a particular disease process, the volume game breaks down and the unit cost increases. This increased cost will need to be shared by pharmaceutical companies, healthcare insurance companies, governments and patients; we suspect the more compelling the health economics a new medicine brings, the bigger share of the profit pool the manufacturing company will be able to capture.
We look to a future in which healthcare will be predictive, preventive, pre-emptive and personalized. In a smartphone-enabled medical system, every person is empowered to be the custodian of their own health, having at their fingertips a hub for medical, diet, fitness, physical environment and location information.
Collecting all this data and analysing it using machine-learning algorithms could pick out trends and interactions at the population level with the hope of achieving the holy grail of healthcare: predict and prevent disease before it occurs.
“It is more important to know what sort of person has a disease than what sort of disease a person has.”