Category | Clinical trial services
Before investigational medicinal products (IMPs) can be approved for use by the FDA or regulatory agencies in other countries, they must go through a series of clinical trials. Clinical trials happen in phases: phase I, phase II, phase III, and phase IV, during which the drug is tested and retested in thousands of volunteers to determine whether it’s both safe and effective for human use. On average, the likelihood of approval (LOA) from phase I for all developmental candidates is 7.9% — that means approximately 92% of IMPs never see approval by the FDA or other regulatory agencies.
So, how can biopharma companies stay competitive and improve their chances of success?
One way is by improving clinical trial (CT) efficiency through digital supply chain optimization, which helps to accelerate speed and reduce barriers in CT planning. Specifically, digital supply chain optimization refers to the use of digital technologies and data-driven strategies to improve and streamline various aspects of the clinical trial supply chain. Typically, it leverages cutting-edge technologies such as data analytics, artificial intelligence (AI), and automation to enhance the efficiency, accuracy, and resiliency of the clinical trial supply chain and facilitate progression of the CT through every phase.
To offer insight into the importance and potential impact of digital supply chain optimization, Deborah Knoll, Director of Clinical Supply Optimization Services at Thermo Fisher Scientific, recently shared her thoughts on the ever-changing industry.
Q: What key clinical trial supply planning challenges do today’s biopharma companies face?
A: For the sake of this discussion, we’ll focus on the supplies required for clinical study. Typical challenges facing clinical supply planners fall into three main buckets: demand versus available supply (speed of production and location and pace of study progression), regulatory (import/export, documentation, and labeling), and environmental. Newer challenges for supply planners are associated with limited supply chain capacity and accelerated timelines.
Regarding demand versus available supply, enrolling an appropriate pool of participants who meet specific criteria while ensuring race, ethnicity, age, and gender diversity can lead to unexpected delays and increased costs. Production and distribution schedules often depend on planned enrollment rates. If enrollment moves slower than expected, clinical supply planning will be impacted since delays may cause excess drug or product inventory. This surplus can lead to increased storage costs, potential product waste, and financial strain. If enrollment is faster or landing in different locations than expected, there is a risk that supplies won’t be available for subjects. That’s why it’s so important to quickly adapt clinical trial supply planning to match the actual enrollment pace; doing so helps to manage resources efficiently, minimize unnecessary waste, and ensure the continuous availability of study materials throughout the trial’s duration.
Receiving regulatory approval from the appropriate health authorities (e.g., FDA in the United States, EMA in Europe) is a critical step in any clinical trial, and regulatory challenges in clinical trial supply planning include stringent GMP guidelines, region-specific labeling and import/export requirements, and the need to adapt to evolving local guidelines for the movement of materials. Additionally, the storage and distribution conditions of the products play a vital role in ensuring that high-quality medicines are administered throughout the process. These complexities require access to expertise to plan for compliance through meticulous record-keeping, tracking systems, governance, and customization of the supply strategy.
Environmental challenges in clinical trial supply planning — stemming from things like weather incidents, significant global events (e.g. Covid-19), and regional disasters — pose significant threats to clinical trial continuity. Severe weather, such as hurricanes and floods, can disrupt material transport, leading to delays and spoilage risks. Natural disasters may even shutter manufacturing and clinical sites, impeding supply chain movement and patient recruitment efforts. Unforeseen events like wildfires, earthquakes, or armed conflict can hinder drug production and distribution, and even relocate subjects, further jeopardizing patient continuity in the study. Thus, planners must create robust contingency plans and build resilient, disaster-resistant supply chains to mitigate these risks.
In today’s drug development process, the objective is to keep the supply chain lean and to complete the clinical trials as quickly as possible. Through experience, we’ve learned what’s needed to achieve fast recruitment and gain prompt approval; the key is to incorporate successful digital optimization strategies to ensure that supplies are available when and where needed, and keep the clinical supplies off the critical path.
Q: What is digital supply chain optimization and why is it so impactful for the industry? Why are traditional (manual) clinical supply chain management practices insufficient for modern-day requirements?
A: Digital supply chain optimization refers to a supply chain that takes advantage of digital technologies and data analytics to help enhance decision-making, improve performance, and respond to fluctuations in real time. Additionally, digitalization provides a platform for effectively interfacing with internal and external tools and platforms, collecting, analyzing, and utilizing data to enhance the efficiency, visibility, and overall performance of a supply chain.
Regarding clinical trial supplies specifically, an example of digital supply chain optimization is receiving real-time electronic updates on the status of clinical trial supplies — from their temperature and location to arrival times and potential delays — thus allowing supply chain managers to quickly assess the status of products and deploy necessary contingency plans to ensure the continuity of supplies for patients.
Another opportunity for digital optimization is with recruitment data; if supply planning systems are automatically updated with enrollment numbers and inventory statuses, planners can move forward with clinical trial planning more efficiently. With access to AI during the design phases to predict enrollment rates and locations, the clinical supplies can be planned more precisely, reducing waste and improving speed.
When data is siloed and planning requires manual processes to move information from one system to another to make decisions, the CT planning is limited by the scope and speed of data availability. The digital aspect of supply chain optimization and automated data analytics allows for things to move seamlessly, without relying on humans to transfer information. Digital supply chain optimization empowers biopharma companies to transform their supply chain operations from reactive and manual processes to proactive, data-driven, highly efficient, and scalable systems.
Q: How does digital supply chain optimization enable more robust supply planning and trial execution? What is Thermo Fisher Scientific doing to implement digital solutions to support the expectations of accelerated pace?
A: Digital supply chain optimization enhances supply planning and trial execution by providing real-time visibility into every step of the supply chain to improve demand forecasting, inventory tracking, risk management, and production scheduling. It also provides the ability to monitor and adapt to changing conditions as they arise; this results in a more robust and responsive supply chain that can meet customer demands efficiently and cost-effectively.
Additionally, the ability to look at past data trends is paramount. For example, if a clinical trial was conducted in a certain country or site in the past, digitalization enables sponsors to look back at that trial’s enrollment data and assess whether a new trial in that country or site will follow a similar pattern. Additionally, historical data can be essential for ensuring supply plans account for compliance with regulatory agencies. For instance, if you know the typical transit time for drug products to move through customs in a particular country, you can plan for that accordingly.
Thermo Fisher Scientific is assembling the building blocks of data pools (i.e. enrollment forecasting and tracking, supply plan modeling, supplier data, and shipment statistics) and building connections between the data pools. Allowing systems to interact enables supply planners to have the necessary data right at their fingertips.
Q: Specifically, within the clinical supply optimization service capabilities, have you made any recent changes to move toward a more digitally connected clinical supply chain?
A: Thermo Fisher Scientific is implementing a supply planning system that enables harmonized processes that can link to inventory and enrollment data. This system provides improved reporting and data aggregation compared to manual processes and allows for advanced analytics, drill downs, historical datasets, and visualizations. By using this system, a supply planner can model multiple scenarios to help plan for potential study changes or create mitigation strategies that facilitate decision-making.
This study forecasting and demand planning process is supplemented by existing simulation services. Through a software partnership and trained resources, Thermo Fisher Scientific offers a powerful tool to evaluate ranges of various study parameters such as enrollment rates, treatment durations, and drug demand variability to help identify risks and plan for mitigations.
Q: Let's talk about simulation strategies as a service. How do they inform an optimized supply plan and help mitigate the risks of modern-day clinical logistics?
A: Simulation is an exercise that allows study teams to plan for what’s likely to happen versus what might happen and make informed decisions. This has multiple implications for trial supply planning. Unlike traditional forecasting practices that treat the model parameters as fixed values in each scenario, simulation treats the parameters as random variables, with ranges and probabilities. Essentially, numerous scenarios are generated by running multiple iterations of known parameters, and the outcomes are then observed. This process generally includes several rounds of fine-tuning to reach an optimized supply plan that covers all phases of the study and balances risk and cost.
As the trial continues, actual data can also be fed back into the model to reassess the output and actions. For example, if the original model predicted it would take 10 months to recruit the required number of patients, but it only took seven months, that information can be utilized to predict what other parameters that three-month adjustment is likely to impact.
The computer software is essential here, because it can quickly run thousands of iterations, whereas that would be impossible for a human to do. Having this process supported by trained simulation operators makes for the most efficient experience, since they can ensure the simulation parameters align with the trial design and assumptions, and then feed information into the demand and supply planning program.
By running multiple iterations of a model using random inputs, simulation provides a comprehensive understanding of the potential outcomes and their probabilities. Additionally, the application can be deployed to simulate multiple scenarios and evaluate the impact of various factors on drug supply. Clinical supply planners can then identify the most cost-effective strategies. This includes determining the optimal batch sizes, production schedules, and inventory levels to meet patient demand while minimizing waste and excess inventory.
By considering uncertainties and variability, simulation enables supply planners to make more robust and efficient decisions, leading to improved resource utilization and cost savings. Overall, the use of simulation to supplement clinical supply planning enhances decision-making processes, reduces risks, and improves the efficiency of drug supply operations.
Q: How can biopharma companies implement digital supply chain optimization strategies?
A: Before any type of implementation, biopharma companies should conduct a thorough analysis of their current supply chain processes to identify potential areas of improvement through digitalization. Then, they should invest in digital technologies that align with their specific optimization goals, including supply chain management software and hardware, data analytics and artificial intelligence tools, and cloud-based collaboration solutions.
Decades ago, biopharma companies had their own manufacturing sites, packaging facilities, and large clinical supply departments — a lot has changed since then, particularly as technology has evolved with complex innovations like simulation and data-linked artificial intelligence. Now, more and more biopharma companies outsource aspects of supply chain management to third parties, so they can focus on other drug development activities. Notably, it’s increasingly common for biopharma companies to partner with a contract development and manufacturing organization (CDMO) or clinical research organizations (CROs) that have specialized clinical trial experience.
Q: Why should biopharma companies partner with a CDMO for digital supply chain optimization?
A: CDMOs and CROs have a wide range of experience in managing key aspects of clinical trials, and they stay up to date on the latest trends, including digital supply chain optimization. Therefore, partnering with a CDMO for digital supply chain optimization allows biopharma companies to leverage the CDMO’s specialized expertise, data-driven technology, and skilled resources to help streamline supply chains, reduce risks, ensure compliance, and ultimately bring their drugs to market more efficiently and effectively.
Q: Why is partnering with a CDMO/CRO useful even for companies that have their own in-house capabilities?
A: While the use of CROs and CDMOs for managing the clinical supply chain has increased over the years, there are still companies that maintain an in-house clinical supplies function. Partnering with a CRO/CDMO can still be useful to manage surge capacity needs, access specialized expertise, leverage advanced technologies, and avoid costly investments in systems, resources, or process training.
By limiting the need for sponsors to heavily invest in trained personnel, evolving technology, expensive equipment, or physical space, they can use the CRO/CDMO to add scalability and flexibility to their development roadmap. The sponsor can benefit from the CRO/CDMO’s industry-leading knowledge and years of experience working in the clinical trial landscape, while simultaneously focusing on activities within their core competencies.
Overall, it’s about taking advantage of technology-enabled expertise; quality CROs and CDMOs are constantly adding new technology, and they typically have the capacity and capability to do things over and above what a biopharma company can do by themselves. Additionally, they employ a team of trained industry professionals who are well-versed in digital supply chain optimization practices, who can effectively apply these technologies to enhance efficiency and support decision-making on behalf of the sponsors and studies they support.
Q: Any final thoughts?
A: To bring new medicines to market, evidence of safety and efficacy must be documented in clinical study data. To efficiently complete the clinical studies and gather the data, trial subjects must complete the dosing according to the clinical protocols. In the end, it’s critical that patients involved in trials get their medication when and where they need it. Leaning on interconnected technology and expertise is the best way to enable success.