Efforts to bring new therapies to market have never been more challenging than today, with drug sponsors striving to manage dynamic, transformative changes in drug development. Advances in data mining and analysis are met by a growing number of data sources. At the same time, digitization has enabled the patient-centric design of trial protocols with an increased focus on the patient experience during clinical trials. We can better segment disease/patient populations and collect better quality data, garnering real-world evidence for regulatory approval. Drug sponsors follow master protocols while conducting adaptive trials designed to allow modifications to a trial or alterations to the statistical procedures without affecting a trial’s integrity and validity. Novel approaches to drug development are especially acceptable to both drug sponsors and regulators when seeking suitable therapies to tackle cancer or other rare, life-threatening diseases.

Such transformative changes call for corresponding changes to current operating models, along with changes to capabilities, infrastructure, and partnerships. They require more investments where the ROI has been declining for years. For lasting change, clinical trial stakeholders could share data standards, clinical data, and access to a limited pool of clinical trial participants. They can also come together to resolve evidence hurdles even as they curate real-world data sets and pilot new approaches to reaching planned endpoints. Some companies are also coming together to share historical trial data to build historical control arms and develop industry-wide data standards and metadata in priority therapy areas.

Transformative approaches to clinical trials seek to speed up the development of effective therapies while maintaining high levels of quality in the research output in various ways:

Advanced Statistical Techniques

By adopting advanced statistical techniques to analyze the data collected and curated, clinical trials teams improve efficiency and the probability of succeeding despite growing data sources. However, researchers must work collaboratively with regulators when adopting transformative approaches to ensure that the geographies they plan to target are modernized sufficiently and are receptive to the change.

Adaptive Trial Designs

The FDA has made it possible for drug developers to reach their endpoints while learning, continuously adjusting, and reducing cycle times using adaptive designs. The FDA encourages drug sponsors to adopt innovative clinical trial designs such as platform trials, basket studies, adaptive trials, and pragmatic randomized controlled trials. Basket trials employ a targeted therapy for multiple diseases linked to an aberration in the genetic structure. Umbrella trials test more than one targeted therapy for a specific disease. Platform trials employ multiple therapies for a single disease to evaluate the efficacies using a decision algorithm to determine their effectiveness. Such trial designs reduce research costs and time, allowing for parallel evaluation of multiple treatments within a single clinical trial structure.

Patient Advocates

The trials enable researchers to efficiently answer multiple questions while allowing patients to navigate the complexity of clinical trials in an optimal manner. Meanwhile, patient advocacy groups are pitching in to get trials designed around patient-centric endpoints and helping to gain buy-in from important stakeholders like payers, physicians, and regulators.

RWD/RWE

Patient and disease populations are segmented better as scientific data is combined with real-world data from patient registries, electronic medical records, imaging, and claims data. This approach helps to understand diseases better and explain, influence or predict their outcomes. The availability of excessive amounts of data may require researchers to narrow their focus, creating a robust data ecosystem to derive any insights with statistical significance.

Real-world evidence from external control arms (ECAs) was initially valuable when putting some patients on placebo was neither ethical nor practical. ECAs could provide insight into the actual standard of care better than any clinical trial and helped reduce development times by helping to decide in early phase trials whether to proceed with later stage trials or not. They take less time to prove the safety and efficacy of a product and save time and costs of running actual clinical trials.

Clinical Trial Simulations

Designing a trial protocol to decide treatment duration, the number of visits, and recruitment targets for a site and simulating its feasibility helps clinical trial teams to reach their milestones faster. Using modeling and simulation (or a digital twin) to assess the impact of inclusion and exclusion criteria helps companies save time and achieve trial milestones more efficiently than earlier methods.

Artificial Intelligence

These transformative approaches benefit extensively from the use of AI, whether to normalize data from different data sources or to curate unstructured data types, whether as images or clinical notes. Machine learning algorithms can automate much of the analysis, provided you have the right technology capabilities.

If managed right, these approaches can be leveraged efficiently to reduce the time needed to move a therapy from application stage to initial approval. They could help someone evaluate multiple therapies in parallel using a master protocol, segment patients better to accelerate recruitment, use adaptive designs to save time, or reduce approval times using RWE while ensuring the safety and efficacy of the therapies. It is high time clinical teams stepped out of their comfort zones to try and adopt one or more of these transformative approaches and reap the benefits these innovations offer.

About MaxisIT

At MaxisIT, we clearly understand strategic priorities within clinical R&D, as we implement solutions for improving Clinical Development Portfolios via an integrated platform-based approach. For over 17 years, MaxisIT’s Clinical Trial Oversight System (CTOS) has been synonymous with timely access to study-specific, standardized and aggregated operational, trial, and patient data, enabling efficient trial oversight. MaxisIT’s platform is a purpose-built solution, which helps the Life Sciences industry by empowering business stakeholders. Our solution optimizes the clinical ecosystem; and enables in-time decision support, continuous monitoring over regulatory compliance, and greater operational efficiency at a measurable rate.