Global Clinical Trials - Pitfalls, Challenges, and Future perspectives

Clinical trials play a pivotal role in the development and evaluation of new medical treatments, drugs, and therapies. These trials are subject to strict regulatory oversight to ensure participant safety and data reliability. The constant updates and improvements by Health Authorities concerning the safety and ethical well-being of individuals make the clinical trial application process challenging.

The regulatory landscape of clinical trials is a multifaceted process that involves numerous stakeholders, including researchers, sponsors, ethics committees, and regulatory authorities. It is crucial that all parties involved adhere to ethical principles and regulatory standards to ensure the validity of trial results and the safety of participants.

The regulatory perspective is essential to maintaining the highest standards of safety and efficacy in healthcare interventions. It ensures that innovative treatments are brought to the market after thorough scrutiny, reducing risks to patients and advancing medical knowledge. By following proper procedures and maintaining transparency, clinical trials contribute to the development of new and improved treatments, benefiting individuals and society.

CTA lifecycle:

Current challenges in the Clinical Trial market:

Clinical research has advanced to unprecedented levels in recent years. Drug research is costly, fraught with failure rates, and has significant societal and financial costs. To maximize clinical trial efficiency and provide the highest calibre of evidence, interdisciplinary cooperation efforts are necessary because most data on chronic diseases are stored in data silos.

Following are the current pitfalls for a Clinical trial research:

1. Regulatory Complexity: Navigating the complex regulatory landscape across different countries and regions can be challenging. Assessing a trial accurately, readers of a published report need complete and clear information. Study reporting may include submission of clinical study reports (CSRs) to regulators, reporting to public clinical trial registries and other means of disclosing study results to stakeholders.
2. Data Management: Managing and analysing the vast amount of data generated during a clinical trial can be overwhelming.
3. Ethical Considerations: Ensuring the ethical treatment of participants and obtaining informed consent is paramount.
4. Budget Constraints: Clinical trials can be expensive, and managing costs can be a significant challenge.
5. Data Privacy and Security: Protecting patient data and ensuring compliance with data privacy regulations is crucial.
6. Document Formatting: Aligning the clinical data as per clinical guidelines and ICH GCP with correct formatting to be compliant with country specific regulatory standards.
7. Stakeholder Management: Coordinating with vendor/ contractors for extended support related to document translation and archival, assistance with maintenance of application/software’s along with maintaining client confidentiality.
8. Clinical Trial Management: Tracking of clinical trial data, document archival in master file, translation of document for proper language pair and completing the study package.
9. Challenges with AI: The biggest challenge in the AI-enabled drug research and clinical trials market is managing the deployment and operating costs of these new systems, which are very difficult to operate and maintain. Lack of skilled labour is also likely to be a barrier to the adoption of AI-enabled drug discovery and clinical trial systems worldwide. Companies operating in the AI ​​drug discovery and clinical trials market should address these challenges to unlock full growth potential in the coming years. ⁽²⁾

Strategic Partnerships for Enhanced Outcomes:

With current regulations such as EUCTR and CTIS for the specialty market in the EU and US, strategic partnerships are essential for adapting to modern business challenges. These partnerships include training in these areas and enhancing business capabilities with the latest software for handling regulatory documentation. They also involve understanding multicentric, multilingual, and global dynamics of clinical trial regulations, supported by industry professionals who are visionaries in trial requirements.

This approach demonstrates that the strategic vision is segmented and integrated throughout the entity, with ongoing support for practices and procedures critical to strategy development and implementation.

Support Road map:

Future of Clinical trials:

To enhance population health, new approaches to research are needed to improve patient engagement and generate the best possible evidence for the quick transition of diagnoses and treatments from research to clinical settings. The coronavirus disease 2019 (COVID-19) has forced clinical trials to become more patient-centric, which has accelerated the development of next-generation trials.

Clinical trial design development is aided by master protocols, which include sub-studies including umbrella studies, basket studies, platform studies, and master observational trials. To provide more realistic, internationally harmonized, standardized real-world tracking of patient experiences and to enable remote monitoring, future trials would be more decentralized, virtualized, and feature digitalized endpoints.

Over time, collaborative trials including academic institutions, patients, government agencies, cooperative groups, pharmaceutical companies, regulatory bodies, and contract research organizations (CROs) would enhance the state of research organisation.

AI-Enabled Drug Discovery and Clinical Trials:

The global market for AI-enabled drug discovery and Clinical Trials is estimated to be 563,85 million in 2022 and is projected to grow at a CAGR of 26.53% to reach 4,643,75 million by 2031.

Artificial intelligence (AI) and machine learning (ML) technologies can automate a variety of tasks, predict patient outcomes, and identify patterns in large data sets. These technologies can help in patient acquisition, improve trial design, and streamline data analysis. With AI-powered algorithms that can process large volumes of data quickly, researchers can make evidence-driven decisions. ⁽¹⁾

AI & ML in Clinical Trials ⁽²⁾:

Machine learning (ML) is the part of artificial intelligence that deals with applying algorithms to data, allowing the system to "learn" and evolve. ML allows users to process large amounts of data and make intelligent inferences and predict outcomes. This knowledge can be used to automate parts of the system, resulting in a faster and more efficient clinical trial system. Automation allows ML predictions to be fed back into the system and specific actions taken, reducing the need for human intervention, improving quality and speed. ML and automation can be applied at every stage of the testing process.

1. Study Design:

Machine learning can be applied to protocol design and language translation. The system can create a protocol for a new test using existing protocol data and health libraries for specific treatment areas. ML algorithms could create an optimal protocol of the database, which would lead to a reduction in design times and protocol changes and interruption of research. Language translation could also be done quickly and easily and with greater accuracy than traditional methods since the ML model would have a region-specific language database to learn from.

2. Study Setup:

ML can be used to automate the design and implementation of a case report form and research database. Using a protocol-based library of CRFs for specific treatments and study designs, an ML model can be trained to design an optimal CRF with editing controls. Automation allows this output to be converted to a real study setup and validated, allowing database designers to modify the design as needed. This approach leads to an optimal design that includes editing controls that might otherwise be missed in a human design.

3. Trial Management:

A lot of automation related to machine learning can be done in experiment management. Some obvious cases include:

a) Site selection:

In which these models can be trained to review site parameters such as Enrolment, Safety, Compliance and Data Quality and predict which sites would be good candidates for a new study for a particular specialty.,

b) Patient registration:

Predictive analytics for patient enrolment is a popular case. It uses variables such as treatment area, study duration, disease (from health economics), study complexity, adverse events, randomization, multicentre, etc. The ML algorithm looks at all the above variables and selects the ones that have the biggest impact (the most important).,

c) Risk monitoring (RBM):

Clinical trial risk-based monitoring, or RBM, can be applied at various stages of trials to identify and mitigate risks affecting the clinical trial. One type of RBM uses some components of site selection, such as enrolment, safety, compliance, and data quality, as well as other variables such as treatment area, study multicentre, etc., to predict site performance during a clinical trial. These predictions can be used to reduce the risk of an experiment by identifying the risk in advance and working to mitigate it, either by closing some sites and opening new ones, or by focusing on sites that work well. ⁽²⁾

EMA is seeking stakeholder feedback on the draft reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle via an open consultation concerning human and veterinary medicines which includes the use of Artificial intelligence in the field of Clinical Trials. The FDA is committed to supporting the use of DHTs in clinical drug development and has developed a comprehensive program to engage with stakeholders in this important scientific area ^[3,4]

In summary, the results indicate that for clinical trials to be successful, changes must be made to trial design, conduct, assessments, and documentation. These changes must also maximize the use of digital, AI-based data and technology breakthroughs, and they must close the gap between the trial and the real world.

Future clinical research development's primary problems will be realizing the promise of multidimensional, real-world evidence generation through the retrieval, assimilation, and analysis of massive datasets and data gathered using cutting-edge scientific methods.

In addition to using social media and online platforms to raise awareness and encourage community involvement, there is a need to expand global financial relationships.

Embracing these trends will foster a robust clinical research ecosystem, leading to improved medical treatments and better patient outcomes. As the landscape continues to evolve, staying informed about the latest regulatory documentation from health authorities will remain crucial for researchers, sponsors, and stakeholders involved in clinical trials.

Regulatory bodies worldwide are proactive in providing guidance to support these trends and ensure that clinical trials are conducted safely and efficiently. As technology advances and the healthcare industry embraces innovation, clinical trial applications will continue to evolve, promoting better patient outcomes and advancing medical science.

References:

1. https://www.insightaceanalytic.com/report/global-ai-enabled-drug-discovery-and-clinical-trials-market-/1239
2. https://www.clinion.com/insight/ai-and-automation-in-clinical-trials/
3. https://www.ema.europa.eu/en/documents/scientific-guideline/draft-reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf
4. https://www.fda.gov/science-research/science-and-research-special-topics/digital-health-technologies-dhts-drug-development

Author Bio

Mallikaarjunan R

Malli has 20+ years of experience in Regulatory Operations and manages Navitas Life Sciences’ Regulatory Services and Technology. He brings rich experience in Pharmaceutical; Consumer and Medical device portfolio having worked with top 10 pharmaceutical companies across the globe helping to develop/support regulatory strategies, operational improvement, program management, predictive forecasting, develop automation/RPA, and resource plans to manage the supply and demand of the workforce.