Early Phase Clinical Trials Maxims

Analyze all pre-clinical data and optimize the study design

The moment when a new compound is ready to be tested in humans is exciting but challenging. It means the drug has successfully passed a battery of pre-clinical tests, but based on these, many important decisions for its clinical development need to be made such as:

• Safety conditions of the trial for the volunteers
• Study feasibility
• Biomarker choices

To predict a drug’s behavior in humans, all relevant pre-clinical data should be thoroughly analyzed. Often, more data is needed than what is strictly required by the regulators depending on the pharmacological and safety profile. For study design there is also not one-size-fits all.

To learn more about how to analyze all pre-clinical data, and optimize study design with concrete case studies:

Working closely with regulators is a key strategy for successful drug development, not a barrier to be sidestepped or overcome.

Building regulatory advice into a trial program is an effective strategy to mitigate the regulatory risk inherent in product development and improve the likelihood of early product approval.

Requesting scientific advice from the right regulatory body at the right time has become an essential tool to guide product development and to have answer on many aspects of the development program:

• Have I chosen the correct and most appropriate animal model? And if there is no pre-defined animal model, is my proposal appropriate?
• Is the manufacturing process well defined and appropriate?
• Have I well classified my IMP?
• Have I selected the appropriate comparator for my patient studies? Placebo/other treatment?
• Are there financial incentives for small and medium-sized enterprises (SMEs) to seek scientific advice from the European Medicines Agency (EMA)? How does it work with the US Food and Drug Administration (FDA)?

To learn more about scientific advices from regulators with concrete case studies:

Careful decision making is essential to minimize clinical development time, manage costs and improve the probability of commercial success.

Modeling and simulation (M&S) can be a very useful strategy to mitigate risks and to make better informed decisions.

M&S involves modeling compounds, mechanisms and disease level data based on historical observations and existing real life data. Computer simulations are run on these models to generate information that can be used to predict outcomes, thereby improving the quality, efficiency and cost-effectiveness of decision-making, both for internal and regulatory purposes.

M&S can be of help in several areas of drug development:

• Can I optimize the starting dose of my first-in-human trial by extrapolating non-clinical data?
• Is it possible to accurately estimate complex drug-drug interaction (DDI) profiles of a compound in silico and explore its potential effects on, for example, cytochrome P450 enzyme (CYP)?
• Could the inhibitor effect at the site of metabolism (gut, liver, or any tissue) be predicted?
• Can I predict drug behavior in pediatric patient populations to support a pediatric investigation plan (PIP), based on my existing adult data?

To further discover Modeling & Simulations uses in early phase clinical trials with concrete case studies:

“If anything can go wrong, it will go wrong” and “everything takes longer than expected” are two important laws by Murphy and Hofstadter, two eminent researchers into time and risk management, that also apply in clinical research.

When planning a trial, one should keep in mind that things do not always go smoothly. Early phase clinical trial protocols tend to become more and more complex, focusing on gaining as much scientific insight as possible.

Many study aspects need to be evaluated from a practical point of view. These include:

• The recruitability of the exact study population
• Investigational Medicinal Product (IMP) preparation steps
• The use of sentinel dosing groups

For a successful trial execution and to learn more about Time and Risk management through efficient protocol design read our two new case studies:

Recruitment and retention issues result in trial delays and costs and may potentially undermine trial results.

Every clinical trial phase is facing its own specific hurdles. The fact that early phase trials do not offer therapeutic benefit for the subjects is a specific drawback. Phase 1 trials are typically performed in healthy volunteers, whereas from phase 2 onwards patient populations are needed. However, special populations and patient cohorts are increasingly included early on.

To assure recruitment in a timely manner it is important to tackle hurdles at all stages of trial preparation and execution and respect the following measures:

• Simplify the protocol if possible to lower the burden for patients and physicians
• Evaluate eligibility criteria to identify overly restrictive components
• Identify factors that impact recruitment, both positively and negatively
• Foresee a realistic recruitment period, leave some flexibility

To ensure efficient subject recruitment for your study, read our complete technical bulletin with two case studies:

To estimate a drug’s clinical potential, information on safety, pharmacokinetic and pharmacodynamic outcomes are all paramount.

The development of new pharmacological compounds can be lengthy and very costly, with many failure risks even during later phase clinical trials. Via translational approaches, including innovative techniques such as human challenge modelling and relevant biomarkers, critical information on a compound’s dose-response effect can be acquired in the early clinical phase.

However, these render phase 1 trials more complex and challenging. An experienced team must assess the following points:

• The research question must be clearly justified and weighed to alternative methodologies
• The proposed methods must be appropriate and provide a meaningful, valid answer
• The method must be as safe as possible and appropriate management of possible risks in place

To learn more about innovative techniques with a concrete case study:

The clinical trial protocol, as a detailed outline of a clinical study, must be rigorously followed by the clinical trial team, not only to be in line with regulations, but also to avoid harm to participants and erroneous conclusions.

As early phase trials are becoming increasingly complex, the risk of protocol deviations, necessary amendments, incorrectly gathered data and even inaccurate conclusions may increase.

To develop a solid protocol:

• A stepwise process must be followed involving various stakeholders at the right time
• Crucial decisions need to be made to avoid possible pitfalls in a complex study design

To learn more about protocol compliance with concrete examples:

All clinical trial activities are required to follow Good Clinical Practice (GCP) principles.

Compliance is regularly checked by regulatory authorities, via ad hoc inspections. Such inspections can be very stressful as all documentation needs to be complete and accurate, and there is always a fear that a non-compliance may be raised.

The objectives of GCP inspections are to:

• Verify that quality assurance arrangements exist, in compliance with regulatory requirements and GCP
• Ensure the safety of human subjects was preserved and ethical standards applied
• Verify that clinical trial data and results are scientifically valid and accurate

The only way to avoid hectic preparation is to be inspection ready at all times. Discover how this can be achieved.

Clinical trials are intended to find answers to a research question by generating data to prove or disprove a hypothesis.

First-in-Human (FIH) and most Phase 1 trials by their very nature are exploratory, without a statistical hypothesis. They are aimed at obtaining reliable information on the safety, tolerability, pharmacokinetics and mechanism of action of a drug.

Various data is captured from FIH trials. Despite their exploratory character, this data needs to be relevant, accurate and appropriately analyzed to obtain meaningful results that are useful for future clinical development.

There are four main steps in clinical data processing:

• Data Planning: Study Protocol, (e)CRF
• Data Collection: Study Protocol/Manuals
• Data Management: Data Management and Review Plan
• Data Analysis: Statistical/PK Analysis Plan, MOCK TLFs

To learn more about early phase clinical trial appropriate and reliable data with concrete examples: