Drug discovery target reviewTaylor Mixides of Infosys Consulting interviews Neil Thomas, Partner of Infosys Consulting and Head of Healthcare and Life Sciences, EMEA, on personalized precision medicine, its advances, challenges and future, exclusively.
Personalized precision medicine (PPM) relies heavily on the use of data and artificial intelligence (AI) to tailor treatment plans to individual-specific biomarkers, predict response to specific treatments, and analyze genetic makeup. and targeting diseases based on environmental factors. PPM has great potential to improve patient outcomes, but it also faces challenges such as data management, security, and rising costs of R&D.
What is personalized precision medicine and how does it differ from traditional medical approaches?
Neil Thomas (NT): Personalized precision medicine (PPM) is medicine that provides personalized treatment based on a patient’s genetic makeup, lifestyle, etc. This type of healthcare relies heavily on the effective use of big data and AI.
Simply put, PPM is tailored to individuals and their biomarkers compared to traditional medical approaches that treat everyone the same way. Traditional mass market medicines are not personalized and optimized like PPM. PPMs can target specific diseases, depending on an individual’s genetic makeup and environmental factors. It can predict how an individual will respond to a particular treatment, thus avoiding the “try and see” approach used in some cases when prescribing mass-market drugs.
How will big data and AI contribute to the development of PPM?
NT: PPMs rely heavily on an individual’s data to make informed and personalized decisions based on the large amounts of data stored in various healthcare facilities. This is where blockchain technology really shines, enabling the combination of big data and AI to develop highly personalized healthcare at scale.
Personalization is often costly, but AI could reduce the cost of drug development for highly personalized medicine by enabling researchers to more accurately predict drug efficacy and safety. can be reduced. By analyzing vast amounts of data, including genetic data, medical history, and drug response data, AI can identify biomarkers and other indicators that can predict how individual patients will respond to particular drugs. increase.
This reduces the need for costly clinical trials and helps researchers identify promising drug candidates more quickly. AI-enabled hyper-personalization approaches also help researchers design more targeted and efficient clinical trials, reducing the cost and time required to bring medicines to market.
What are the challenges faced in implementing PPM in therapeutic drug discovery?
NT: The vast amount of data available is both an opportunity and a great challenge. This data is often stored in a variety of locations, such as hospitals and medical centers, and each healthcare facility uses different privacy protocols, so security is a serious concern.
Precision medicine beyond oncology
Another pervasive problem is the rising costs of research and development across the industry. This means that budgets are becoming increasingly squeezed, leaving fewer opportunities to invest in innovative new methods of drug discovery and delivery. Not only that, but patients and governments want more for less, especially in this new era of easy access to personalization in most other areas of our lives.
how can lifeCan style factors such as diet and exercise be incorporated into the PPM treatment plan?
NT: I believe personalized and precision nutrition is the logical next step for the broader health industry. It may complement PPM as a broader preventative measure to improve overall personal health.
What does the future of PPM look like and how will it impact the drug discovery industry as a whole?
NT: There is no doubt that the new PPM paradigm will fundamentally change the industry. Disease prevention, rather than cure, is the focus of both manufacturers and providers, leading to improved health for many people.
However, this requires a reality that is very different from the current reality. Data must be managed securely to ensure that the correct information is used for its intended purpose. This means that the links between individuals, healthcare providers, manufacturers and regulatory bodies will be much greater and closer.
However, the exciting element of PPM is the ability to rapidly arrive at precise treatments that improve patient care through core analysis of existing datasets. Drug discovery in its truest sense is a data component of clinical trials, and access to clinical trial data has greatly improved, and the ability to process it in an AI-powered way allows for faster relevant efficacy. and can be achieved cheaply. Provides more effective management of disease.
Neil is a Partner at Infosys Consulting and Head of Healthcare and Life Sciences in EMEA. In his current role, Neil leads the delivery of innovative, transformative, high-quality services to healthcare and life sciences organizations looking to deliver superior patient and stakeholder experiences. .