The discussion called out several trends and challenges including:
Patient Centricity in Clinical Development
To conduct a successful and timely clinical trial that produces meaningful results, it is essential to attain sufficient participant enrollment, expedite the enrollment process, increase patient engagement, ensure high patient retention, and improve patient adherence to therapy. Achieving these objectives can be taxing for an early-stage life science company.
It takes significant time and effort to expand clinical trial access to patients in wider geographies. However, a less diverse patient population can lead to insufficient testing of drugs for patient safety and effectiveness. Virtual and decentralized trials that are more accessible to a larger population introduce new challenges relating to managing site supply, data privacy, authentication, and data-sharing between various systems.
Lengthy Clinical Development Timelines
Drug developers closely monitor the drug development timelines and find ways to shrink them. Even today, a large number of life sciences companies are using paper-based process to document and manage their clinical data. This makes it exhausting to collect, analyze and maintain records. Manual data also restricts development of a well-connected supply-demand forecast and execution of informative logistical decisions. Additionally, many companies that collect information on different software platforms struggle to integrate data between systems. Such barriers limit the possibility of reducing the drug development time and using data analytics to its full capabilities.
Increasing Complexity in Study Designs
The challenges of measuring the safety and efficacy of investigational drugs have significantly increased the scope of clinical trials. A vast majority of studies today have multiple end points to assess the effects of the drug. This increases the likelihood of making false conclusions unless appropriate adjustments for multiplicity are made. Moreover, the industry is also observing proliferation of adaptive study designs and multiple iterations based on patient outcomes, making the studies more complex than the traditional fixed designs.
Rapid Changes to Regulatory & Trade Compliance
The life sciences industry is highly regulated. Governed by numerous constantly evolving policies, this industry also witnesses frequent development of new regulations. Inability to interpret them accurately can cause manufacturing process delays and violations, which can negatively impact financial plans and reputation.
Our panel also walked through several approaches to tackling these challenges:
Big Data & Analytics
Using quantitative methods to draw meaningful insights from data improves decision-making at various stages in the supply chain. It can help generate accurate demand and supply forecasts, as well as perform predictive risk management. Data can help improve trial design by identifying compliance issues or streamlining patient enrollment. It can accelerate the trial process by spotting eligible patient groups efficiently and matching patients to clinical trials accurately.
Digital supply chain management systems enhance visibility across the clinical supply chain, reducing the complexity of supply chains. It helps track supplier network activities, enables quick response to any errors or changes to the supply chain management, and makes compliance with the regulatory demands easier.
Integration of People & Systems
Digital technologies can rapidly gather valuable insights from multiple sources of data, increasing the quantity and quality of data collected and improving the quality of patient experience. They can ease data collection for the site staff, automate the process of updating databases, and track shipment or expiry. The new technologies also facilitate real-time interaction between functions such as clinical operations, clinical supply, regulatory and quality. Furthermore, they can help integrate information and operations carried out by various supply chain partners.
Artificial Intelligence (AI) & Machine Learning (ML)
AI and ML can expedite drug discovery, support target identification and validation, make supply chains smarter and more responsive, and automate forecasting amongst their many other benefits. E-labelling, GPS tracking, wearables and sensor blockchain technology are some examples of such tools. Moreover, AI and ML make the trial recruitment process more efficient and effective, expediting patient enrollments. AI tools can help communicate clinical trial value and increase patient health literacy, leading to higher patient adherence and retention.
Digital transformation is inescapable if you wish to adapt to external industry changes. Even small and mid-sized companies with limited capital and human resources can strategize. They can create a path to gather the data needed and begin moving toward a digital supply chain.
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