Is your data strategy set up to tackle key challenges in early clinical development?
In clinical development, a high-quality evidence package is a prerequisite for a new therapy to gain approval from regulators and other key decision-makers. As such, the quality of your clinical data is one of the key factors determining whether an effective new therapy reaches patients.
Implementing a data strategy can help to protect the quality of your evidence package. However, many companies start planning their strategy quite late in the development process, which makes it difficult to address (sufficiently address) the complex considerations involved. As we explore in our new eBook, a data strategy planned well in advance of starting Phase 1 and following the industry’s best practices can help you reduce risk, expedite clinical development, and successfully achieve your business objectives.
Download the new eBook, “Are you Harnessing the Power of your Clinical Data?” to find out how to optimize your data strategy to advance clinical development.
In our previous blog, we talked about the value of planning a data strategy for the entire duration of your program (i.e., a ‘program-wide’ strategy). However, it is also important to plan for specific phases of clinical development, because they each have unique challenges. Below we discuss the major challenges commonly encountered in Phase 1 and Phase 2 studies, and the tactics you can use to resolve them. An upcoming article will engage with challenges in Phase 3 and post-market.
Phase 1 trials: Planning for unexpected risks to budgets and timelines
Phase 1 and 2 trials typically involve assessing the safety, toxicity, and tolerability of an investigational therapy in a small group of (usually healthy) people for the first time. The key aims of Phase 1 include using data to establish proof-of-concept, begin to identify safe dosage ranges, as well as obtain early insight into the pharmacokinetics and metabolism of the drug that can expedite later stages. Although these short-term studies generate lower volumes of data compared to later phases, they can nonetheless be complex to manage. In particular, they can involve various unforeseen logistical and administrative challenges that could jeopardize the success of your projects.
Good data strategies in Phase 1 will help you throughout the development of the medicine or therapeutic, but it requires early upfront investment. It can take up to 12 weeks to build a comprehensive database suitable for the needs of your study. You will also need to implement fitting electronic data capture systems to integrate data from multiple service providers and introduce appropriate data standardization procedures well before the study begins. You may also face the complicated task of making costly protocol amendments, which can even occur further down the line while the study is being conducted.
Consequently, if you do not prepare your data strategy in advance, these challenges will take a lot of time to overcome. In the best case, you will significantly delay your timeline as well as increase costs. In the worst case, if these problems remain unresolved, your program will come to a standstill and you may have to stop your investigation into a new therapy that could have helped many patients.
Therefore, planning a data strategy in advance can help you:
Rapidly tackle the challenges of data as they arise
Stay within budget
Meet key milestones
Expedite the development pathway and shorten your therapy’s time to market
Mitigate risks and streamline processes in your Phase 1 studies
Solutions for overcoming Phase 1 challenges
Working collaboratively with an experienced partner from an early stage in your program can help you identify potential issues and prepare effective ways to resolve them if they do occur. For instance, an experienced statistical team can implement systems to integrate different data sources and build suitable databases at the start of the program. They can also set up a streamlined data review process for each key decision stage and implement a consistent data handling approach to reduce your oversight burden.
Moreover, working with a data management specialist can help you to preserve the integrity of your trial design. For example, if you decide to use an adaptive trial design that alters the study’s parameters midstream, an experienced biostatistician can put processes in place to overcome the complex statistical issues that can arise. This will allow you to gain the many benefits of adaptive designs, while generating high-quality data that will help inform downstream trials.
Phase 2 trials: Plugging knowledge gaps to strengthen your data
Having successfully passed through Phase 1, the investigational therapy is then further examined in Phase 2. This stage of drug development involves studies in patients who have the disease of interest in order to obtain preliminary evidence of the therapy’s efficacy and safety.
Above all, the evidence package that is generated by the end of Phase 2 must be of sufficiently high quality to gain approval from regulators and other key stakeholders. This means you will be able to progress your therapy to Phase 3, out-license it or even sell your company, if that is your business objective.
A carefully planned data strategy can bolster the quality of the Phase 2 evidence package by identifying and addressing any knowledge gaps in your Target Product Profile. These gaps include:
Indications: Which diseases?
Population: Which patients and where?
Clinical efficacy: Does it work effectively?
Safety and tolerability: What kind of adverse events and how many?
Stability: How long can it be stored in the field?
Route of administration: How is it given to patients?
Dosing frequency: How often and how long must it be given?
Cost: Will it be affordable to the target population?
Time to availability: How long will it take to develop?
As you plug these knowledge gaps, it is also important to generate data that satisfies the requirements of payers and health technology assessment (HTA) agencies to ensure your therapy ultimately reaches patients. Fortunately, knowledgeable biostatisticians can use certain approaches to overcome this challenge.
Solutions for overcoming Phase 2 challenges
Quantitative Pharmacology and Pharmacometrics (QPP) methods can generate the data needed to fill any knowledge gaps. For example, expert pharmacometricians can devise modelling approaches to guide your Phase 2 studies, such as providing the optimal range of doses and the best active control.
Additionally, Bayesian approaches can help to revise the data as it accumulates during a trial, such as re-estimating samples sizes and using historical data to determine how a cohort of patients will respond to treatment. This can bring various benefits, such as enabling smaller and more cost-effective trials, more precise dose-escalation, and better patient care, all while minimizing the risk of introducing bias or impairing interpretability.
As planning a data strategy involves many complex considerations, planning well in advance is highly recommended. Following the industry’s best planning practices and tactics will enable you to generate data of the highest quality to increase your chances of gaining approval from key decision-makers and achieving your business objectives. What’s more, having plans already in place and working with experienced partners can help you to immediately tackle issues as they arise, allowing you to minimize costs and delays to your pipeline, and even expedite the development pathway.
As clinical data becomes ever more complex and interconnected, it is vital that we start harnessing the great wealth of information we have at our fingertips so we can ultimately bring more new medicines to patients.
Download our new eBook to discover best planning practices, tips and tactics to optimize your data strategy and boost success in clinical development.