Personalised medicine: The transformative trend revolutionising life sciences 

Personalised medicine: The transformative trend revolutionising life sciences 

In modern-day life sciences, patient-centricity will be the force driving precise medical interventions. Personalised medicine is gaining prominence as it promises to strengthen decision-making on prevention, diagnosis and treatment by decoding an individual’s genetic profile.

Subhro Malik, Senior Vice President and Head Life Science, Infosys

In his piece, Subhro Malik, Senior Vice President and Head Life Science, Infosys, details how personalised medicine, driven by genetic profiling and technological advances, is revolutionising life sciences with tailored medical interventions for individual patients.

In personalised medicine, the focus shifts from treatment and cure to detection and prevention. Doctors can refine ongoing treatment and deliver precise medicine based on the patient’s genetic disposition. In departing from the long-standing ‘one size fits all’ approach of conventional medicine, personalisation gives patients access to medical care tailored to their unique genetic profile.  

For instance, in cancer treatment, targeted therapy drugs can be designed to target the exact genetic mutation that is responsible for the cancer in a patient. These drugs are proving more effective than generic traditional methods such as chemotherapy and radiotherapy. A study published in the NIH’s National Library of Medicine found that the response rate to a particular targeted treatment for chronic myeloid leukemia to be at 90% compared with 35% for conventional chemotherapy.  

A European Commission study discusses a ‘DNA medication pass’ developed by researchers to capture a patient’s unique genetic identification markers. Clinicians can scan the pass to identify the best-suited medication for a specific patient with 30% fewer side effects from the precision drug they then prescribe.  

Personalisation of care powered by technology 

Technological advances such as the availability of massive computing power, data analytics tools, and sensors have enabled several innovations in personalised healthcare: 

  • Predictive analytics and AI/ML 

With preventive care increasingly becoming a priority, genetic testing is an important area of focus. Individuals found to be at a greater predisposition to illnesses such as breast cancer and Alzheimer’s can receive a preventive care plan to ensure a better quality of life. Discarding the conventional trial-and-error method, they can cut down the probability of adverse effects of medication by finding a treatment plan that befits the individual’s genetic profile.  

With advancements in data science-based tools, AI, and ML, raw data is converted to timely insights to determine what works best for a specific profile of patients. Methods that have become established, like genome sequencing, biomarker testing etc., rely on AI/ML models that match biomarker patterns and make relevant inferences on how a disease is progressing for a specific patient.  

  • Digital twins 

A digital twin (data model) of each patient can be generated to accelerate drug development while giving an affordable option for those conducting a pre-trial before the one involving real participants and more investment. AstraZeneca is using digital twin modelling along with in-silico experimentation and simulation to accelerate development lead times and reduce waste. The additional benefit is that any medical intervention can be specifically developed and designed around those actually requiring it.  

  • Computing power and analytics capabilities 

Digital platforms make it possible to store data with fast, easy and anytime-anywhere access, which in turn speeds up decisions for healthcare providers. This can be valuable in complex activities such as genome sequencing that require huge amounts of data. 

The scalable, flexible solutions and massive computing power enabled by cloud-based infrastructure help store and access the right data at the right time. This data is accumulated and analysed across people of different origins, environmental conditions and lifestyles. It is then possible to extrapolate and identify the precise treatment, develop target medical interventions, trace their impact and take corrective action where required. 

Several factors inhibit technology adoption 

Despite the clear advantages, technology adoption in life sciences has been sluggish. A paper by the European Parliament found that breakthroughs in personalised medicine coming through into standard clinical practice were fewer than expected. 

The slow rate adoption rate of cutting-edge healthcare solutions can be attributed to various reasons. Healthcare companies are often strapped for resources, so they are understandably reticent about diverting precious funds and manpower to new technologies. They would rather persist with time-tested and evidence-based methodologies that are also cost-effective. Many healthcare systems continue using legacy IT systems that complicate the integration of new technology. 

Moreover, frontline workers and medical professionals are neither trained nor have the expertise to absorb personalised care applications into day-to-day healthcare. Without appropriate support and training, they will likely resist change and refrain from using new-age healthcare applications. 

Large language models can potentially reduce paperwork, suggest diagnoses and more. However, as these evolve to become more unbiased, accurate, and trustable, so must the ethical aspects to provide adequate guardrails for securing patient data. 

Overcoming adoption challenges to accrue benefits of personalisation  

Today, emerging technologies can help connect everything and build a single loop around the patient’s health journey. The possibilities of tech-supported personalised medicine can alleviate major pain points for the medical fraternity, such as staff retention and patient data security. 

To overcome traditional barriers to adoption, research-based conversations must dramatically shift to implementation-led approaches. Engaging medical practitioners at all levels is crucial for early understanding and buy-in. Involving patients and the public is imperative for broader acceptance and awareness. Finally, fostering collaboration among government entities, policy-makers, healthcare service providers and equipment manufacturers is essential. A united effort from all stakeholders is key to establishing a seamless medical ecosystem powered by data, optimising costs and driving innovation.  

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