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Will Synthetic, AI-Based Digital Humans Change Pharma and Life Sciences? Q&A with Abid Rahman, SVP Innovation, EVERSANA

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Rahman discusses the new opportunities that digital twins present to researchers.

Abid Rahman

Abid Rahman
SVP Innovation
EVERSANA

Digital twins is an exciting new technology that presents researchers in the life sciences industry with the opportunity to obtain highly detailed sets of data. Abid Rahman, SVP of innovation at EVERSANA, spoke with Pharmaceutical Executive about the new technology and how it can be used.

Pharmaceutical Executive:In the most simplistic of terms, what’s a digital twin and why should pharma and life sciences companies care?
Abid Rahman: Digital twins are virtual replicas of physical objects or processes. Sometimes they are called synthetic clones. These can also be created to be human-like characters, developed through AI-based simulations, that to the naked eye, look like humans.

They are trained to sound, act, and convey messages like humans. Their potential to improve communications and connectivity in so many industries is just fascinating, especially in sectors that historically have relied so heavily on face-to-face human interactions like healthcare.

PE:Are these concepts currently being used in the pharma industry and how accurate are they?
Rahman: Many companies are exploring what this could look like. In life sciences, the concept of digital humans can offer a powerful way to simulate and analyze complex biological systems, drug interactions, and patient responses. By creating digital counterparts of real-world entities, researchers can optimize drug development, predict outcomes, personalize treatments, and create customized training and educational programs. They could enhance our understanding of diseases, drug efficacy, patient variability, and HCP prescribing behaviors.

Imagine a scenario where we create a digital twin of a patient’s cardiovascular system. By inputting relevant data (genetic, physiological, and lifestyle factors, that type of thing), we can simulate drug interactions, predict adverse effects, and optimize dosages. This could accelerate the rate of drug discovery, reduce costs, and minimize risks associated with clinical trials. Now you must be careful because these are not real people, but what we’ve found is we can do more with technology today than ever before, and if something like a digital twin could help accelerate a cure for a condition like a form of cancer by 15 years, why wouldn’t we look at it? Just look at the latest news where a genetically engineered pig kidney was altered using genetic testing and AI and a patient went home with a potential new lease on life. Digital twins allow us to safely do many simulations within a short time.

PE:Are there challenges or limitations researchers face when implementing digital twins in life sciences?
Rahman: Without a doubt, validating digital twins with real-world data is crucial. Ensuring accuracy, reliability, and safety is challenging. Additionally, integrating diverse data sources, addressing bias, and maintaining ethical standards are ongoing concerns. Researchers must strike a balance between complexity and usability. But if they can, what opportunity it presents.

PE: What are the regulatory guidelines that must be followed when using digital twins?
Rahman: This is still an emerging field that is being accelerated through generative AI. Rather than wait for specific guidelines, it's a good idea to ensure the principles of regulatory guidelines, designed to ensure safety, reliability, and effectiveness, particularly when they influence patient care and drug development, are considered. In addition, because these are AI-based digital solutions, it's important to adhere to good software development practice, quality assurance, validation, and continuous monitoring.

PE:Does generative AI (Gen AI) play a role in enhancing digital twin technology coming to life in pharma?
Rahman: Gen AI is a crucial element that provides a massive boost to efficiently creating digital twins. It can be used to create synthetic content and augment existing data that is used for creating simulations.Availability of good quality data has always been a challenge in building these systems. Gen AI can also help with data engineering and creating software code that brings digital twins alive. It is vital.

PE: Are there ethical considerations that should be considered when using digital twins in healthcare?
Rahman: As I mentioned earlier, ethical transparency, patient consent, and data privacy are paramount. Researchers must ensure that digital twins do not perpetuate biases or compromise patient confidentiality. Regular audits and adherence to regulatory guidelines are essential.

PE:Do you foresee the adoption and evolution of digital twins within the life sciences and pharma industry?
Rahman: Absolutely, and here’s why. The adoption and evolution of digital twins within the life sciences is not only foreseeable but already underway, with significant potential for expansion and improvement because of Gen AI. Our industry must continue to do all we can to accelerate treatment options to help patients. It is our responsibility to those we serve. To do this, we must carefully explore innovative technologies that can help us. One example I am proud of is the work we did a few years back in the Schizophrenia space. We took anonymized patient experience and journey data along with peer-reviewed papers and used AI to create patient personas and common challenges patients may face depending on their clinical and life circumstances. Based on the AI personas, we created digital patients used for HCP educational purposes.

Similar concepts can be used to help with diagnosis support and creating patient simulations across many disease areas including rare diseases. The adoption of digital twins in life sciences and pharmaceuticals is poised to accelerate, driven by the need for more efficient drug development processes, personalized treatments, and smarter manufacturing and supply chains. The industry's increasing focus on precision medicine and patient-centric care, combined with technological advancements, strongly supports the continued evolution and integration of digital twin technologies.

The full realization of digital twins’ potential in pharma is still on the horizon, the evolution of this technology is likely to have a profound impact on the industry, offering a promising avenue for innovation and efficiency in the years to come.

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