Aiswariya Chidambaram Explains How Digital Transformation Is Revolutionizing Clinical Trials For Pharma & Life Sciences

Digital transformation can mean many things to many people, but most can agree that it involves evolving an organization’s DNA to be digital-first, across workflows, policies & procedures, management, business intelligence, and customer facing activities and communication. Outside of the sector, few realize the importance of digital transformation in the health and life sciences sector; whether for general management, research and development, or even in the mandatory but complex clinical trials process.

Digital transformation has had a tremendous impact on the clinical trials process, where new medicines and treatments must pass field trials to test both efficacy and safety. Digital transformation can enable better data collection, management, and analysis during clinical trials. Electronic data capture (EDC) systems can replace paper-based data collection, reducing the risk of errors and improving the accuracy of data. These systems also make it easier to monitor data quality and manage data across multiple sites.

Digital transformation can also help identify and recruit patients more efficiently. Social media platforms, online patient communities, and other digital tools can be used to identify and reach potential trial participants. This can help reduce the time and cost associated with patient recruitment. Digital tools can enable remote monitoring of trial participants, which can improve the accuracy of data collection and reduce the burden on patients who would otherwise have to make frequent visits to trial sites.

Digital transformation can help researchers analyze large amounts of data more quickly and accurately. Machine learning and other data analytics tools can be used to identify patterns and insights that might not be apparent from traditional analysis methods. All of these can ultimately lead to faster and more effective drug development, which can benefit patients and the healthcare industry as a whole.

To dig deeper into this, I had an in-depth interview with good friend, long-time collaborator, and biotech luminary Aiswariya Chidambaram. She has well over a decade of experience as an analyst, consultant, and subject matter expert with a passion for digital transformation in life sciences. This has led her to become an adept analyst of the latest tools and technologies, helping pharmaceutical companies and emerging biotech firms strategize their vision and roadmap.

Aiswariya has contributed content and authored several articles for many leading trade publications, magazines, and journals. She is known for her ability to provide unique insights into the evolving life sciences industry, particularly in digital transformation, and has played an instrumental role in driving innovation and improving patient outcomes in the industry.

Loren Moss: How has digital transformation impacted the clinical trial landscape in recent years? What are some of the key changes you have observed?

Aiswariya Chidambaram: Digital transformation has had a significant impact on the clinical trial landscape in recent years. One of the key changes that I have observed is the increased use of digital tools and technologies for patient recruitment, engagement, and data collection. Biopharma companies are leveraging mobile apps, wearables, social media, and other digital platforms to connect with patients, monitor their health and wellbeing, and gather real-time data on drug effects.

Another important change is the shift towards decentralized and virtual clinical trials, which use digital technologies to conduct trials remotely and reduce the burden on patients and trial sites. This has enabled trials to reach a larger pool of patients, including those in remote or rural areas, and has also reduced the costs and time associated with traditional site-based trials.

In addition, digital transformation has enabled biopharma companies to improve trial efficiency, data quality, and decision-making using electronic data capture (EDC) systems, data analytics, and artificial intelligence (AI). These tools have enabled companies to monitor trial progress in real-time, identify and mitigate issues early, and accelerate the drug development process.

Overall, I believe that digital transformation has brought significant benefits to the clinical trial landscape, including improved patient outcomes, reduced costs, and accelerated drug development timelines. However, there are still challenges that need to be addressed, such as data privacy concerns, regulatory hurdles, and the need for adequate infrastructure and training to support digital transformation in clinical trials.

Loren Moss: Can you elaborate on the most promising digital tools and technologies that are being used in clinical trials today, and how are they improving trial efficiency and patient outcomes?

Aiswariya Chidambaram: There are several promising digital tools and technologies being used in clinical trials today that are improving trial efficiency and patient outcomes. Here are a few examples:

Electronic data capture (EDC) systems: EDC systems are used to collect and manage clinical trial data electronically. They have replaced paper-based systems and have improved trial efficiency by reducing errors, enabling real-time monitoring of trial progress, and facilitating data sharing across trial sites.

Wearables and mobile health apps: These digital tools are used to monitor patient health and activity levels remotely. They have enabled patients to participate in trials from their homes, reducing the burden of frequent site visits and improving patient engagement. Wearables and mobile apps have also enabled real-time data collection, leading to better insights into drug effects and patient outcomes.

Artificial intelligence (AI): AI is being used to improve trial design, patient selection, and drug discovery. AI algorithms can analyze large amounts of data quickly and accurately, leading to better insights into disease mechanisms and drug effects. AI can also help to identify patients who are most likely to benefit from a drug, leading to more successful trials and faster drug development.

Social media and online patient communities: These digital platforms are being used to recruit patients for clinical trials and improve patient engagement. Social media can reach a large audience quickly and cost-effectively, enabling companies to recruit patients faster and more efficiently. Online patient communities provide patients with support and information, leading to improved patient outcomes and increased patient retention in trials.

Loren Moss: Can you provide some examples of biopharma companies that have successfully employed digital tools and technologies in their clinical trials, and the benefits they have reaped?

Aiswariya Chidambaram: Certainly! Here are a few examples of biopharma companies that have successfully employed digital tools and technologies in their clinical trials and the benefits they have reaped:

Pfizer used a mobile app called “Pfizer CT-Go” in a Phase 3 clinical trial for its breast cancer drug, Ibrance. The app enabled patients to report their symptoms and side effects in real-time, leading to faster identification of adverse events and improved patient safety. The use of the app also reduced the need for in-person visits, leading to cost savings and improved patient engagement.

Novartis used a remote monitoring platform called “Clinical Pipe” in a Phase 2 trial for its heart failure drug, Entresto. The platform enabled real-time monitoring of patient health and allowed clinicians to adjust drug dosages based on patient data. The use of Clinical Pipe led to improved patient outcomes and reduced hospitalizations, as well as cost savings and improved trial efficiency.

Roche used an electronic informed consent (eConsent) platform in a Phase 3 trial for its multiple sclerosis drug, Ocrevus. The eConsent platform allowed patients to review and sign consent forms electronically, leading to faster enrollment and reduced administrative burden for trial staff. The use of the platform also improved patient understanding of the trial and increased patient engagement.

Sanofi used an artificial intelligence (AI) platform called “Clinical Insights” in a Phase 2 trial for its asthma drug, Dupixent. The AI platform analyzed patient data to identify predictors of treatment response and allowed clinicians to adjust drug dosages based on patient data. The use of Clinical Insights led to improved patient outcomes and reduced hospitalizations, as well as cost savings and improved trial efficiency.

Several other leading biopharmaceutical companies including Merck & Co., GSK, Eli Lilly, Moderna, etc. are known to have demonstrated successful digital tools and technologies in their clinical trials.

Loren Moss: What are some of the challenges that biopharma companies face when adopting digital tools and technologies in their clinical trials, and how can these challenges be overcome?

Aiswariya Chidambaram: Adopting digital tools and technologies in clinical trials can bring significant benefits to biopharma companies, but it can also present several challenges. One of the biggest concerns for biopharma companies is ensuring the privacy and security of patient data when using digital tools and technologies. Companies must ensure that patient data is stored and processed securely and that all applicable privacy regulations are followed. This can be addressed by implementing robust data protection measures and ensuring that all third-party vendors and service providers meet data privacy and security standards.

Furthermore, Biopharma faces challenges in ensuring that the digital tools and technologies used in clinical trials meet regulatory compliance standards as regulations and standards can vary between different jurisdictions. To overcome this, it is critical that they work closely with regulatory bodies and seek guidance on regulatory compliance requirements early in the development process.

While digital tools and technologies can improve patient engagement and reduce patient burden, there may be challenges in ensuring patient uptake and compliance. Companies should involve patients and caregivers in the development of digital tools and technologies and ensure that they are user-friendly and accessible to patients with diverse backgrounds and abilities. Nonetheless, integrating new digital tools and technologies with existing clinical trial systems and workflows can be complex and time-consuming. Companies should ensure that any new tools and technologies are compatible with existing systems and that adequate resources are allocated to manage integration and data exchange. Finally, implementing these tools can be expensive, especially for smaller biopharma companies. Therefore, it is important that companies should carefully assess the costs and benefits of adopting new tools and technologies and seek to partner with vendors and service providers who offer cost-effective solutions.

Loren Moss: What role do you see artificial intelligence (AI) playing in the future of clinical trials, and how can it be leveraged to improve trial outcomes and accelerate drug development?

Aiswariya Chidambaram: Artificial intelligence (AI) has the potential to revolutionize clinical trials by enabling more efficient and effective drug development. Here are some ways in which AI can be leveraged to improve trial outcomes and accelerate drug development:

Patient Screening: AI algorithms can analyze large amounts of data, including patient medical records and genetic information, to identify suitable candidates for clinical trials. This can lead to more targeted recruitment and reduced patient burden.

Drug Discovery: AI can be used to analyze large amounts of data and identify new drug targets or repurpose existing drugs for new indications. This can accelerate drug development timelines and reduce costs.

Clinical Trial Design: AI can be used to optimize clinical trial design by identifying the optimal dosage and treatment regimen, predicting patient response to treatment, and identifying potential safety issues. This can lead to improved patient outcomes and reduced trial costs.

Real-Time Monitoring: AI algorithms can analyze patient data in real-time and alert clinicians to potential adverse events or treatment inefficacy. This can lead to improved patient safety and better treatment outcomes.

Predictive Analytics: AI can be used to predict patient outcomes based on large amounts of data, including patient medical history, genetics, and treatment history. This can enable personalized treatment plans and improve patient outcomes.

As mentioned earlier, there are also challenges associated with the use of AI in clinical trials and therefore it is imperative that biopharma companies must carefully evaluate the benefits and risks of using AI in their clinical trials and ensure that they have the necessary infrastructure and expertise to support the successful adoption of AI technologies.

Loren Moss: How can digital transformation help to improve patient engagement and participation in clinical trials, and what are some best practices for implementing patient-centric digital strategies?

Aiswariya Chidambaram: Digital transformation can help to improve patient engagement and participation in clinical trials by providing patients with more convenient and personalized experiences. Below are some of the ways in which digital tools and technologies can be leveraged to improve patient engagement:

Patient Recruitment: Digital tools such as social media, patient communities, and mobile apps can be used to reach a wider pool of potential participants and make the recruitment process more patient centric.

Patient Education: Digital tools such as online educational resources, video tutorials, and virtual reality simulations can be used to provide patients with more comprehensive and personalized information about the clinical trial process and their specific condition.

Patient Communication: Digital tools such as telemedicine, chatbots, and patient portals can be used to provide patients with more convenient and timely access to clinicians and study coordinators.

Patient Feedback: Digital tools such as patient surveys, patient-reported outcomes, and social media listening can be used to gather feedback from patients and improve the patient experience.

Patient Retention: Digital tools such as gamification, personalized reminders, and incentives can be used to improve patient retention and reduce dropout rates.

Best practices for implementing patient-centric digital strategies include: Conducting thorough patient research to understand patient needs and preferences, ensuring that digital tools are user-friendly, accessible, and comply with regulatory requirements, providing patients with clear instructions and support for using digital tools, integrating digital tools into existing clinical trial workflows and ensuring that they are compatible with existing systems, monitoring patient engagement and feedback and making necessary adjustments.

Loren Moss: How do you see digital transformation in clinical trials evolving in the coming years, and what new digital tools and technologies do you anticipate emerging in the field?

Aiswariya Chidambaram:  Digital transformation in clinical trials has already made significant progress in recent years, but there is still much room for growth and innovation in the field. We expect to see an increasing number of clinical trials conducted remotely using digital tools such as wearables, telemedicine, and e-Consent. Decentralized clinical trials can improve patient access, reduce costs, and accelerate trial timelines. Real world data gathered using digital tools can provide valuable insights into patient outcomes and drug efficacy in real-world settings. AI, on the other hand, can be used to analyze large amounts of clinical trial data and identify patterns that may not be apparent to humans. AI can also be used to develop more personalized treatment plans based on patient characteristics and treatment history.

Blockchain is an emerging technology that can be used to improve data security and transparency in clinical trials. It can also be used to track the supply chain of investigational drugs and ensure that they are being used appropriately. Digital biomarkers collected from wearables, mobile apps, and other digital tools can provide objective measures of patient health and treatment response, which can be used to inform treatment decisions and drug development. Given the huge potential for digital transformation in clinical trials, biopharma companies have no choice but to stay up-to-date with the latest developments in the field and prioritize patient-centric digital strategies to stay ahead of the curve in this rapidly evolving market.

Loren Moss: Thank you for your time!