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International Clinical Trials

Setting-Up Success

Geoffrey Banks at ClinPharm Consulting advises organisations on optimising clinical pharmacology studies to produce robust pharmacokinetic and pharmacodynamic (PK, PD) data sets

No one sets out to conduct a clinical study that will not meet its objectives. However, all too often, studies fail to meet their objectives or fail to reach their full potential by generating a less than optimal data set. This is particularly pronounced with clinical pharmacology investigations that involve pharmacokinetic (PK) or pharmacodynamic (PD) analyses. A number of common pitfalls in the planning, conduct, and analysis of studies with a PK, PD component can result in a sub-optimal data set, which can then have a significant impact on future decisions within drug development programmes. Obtaining a robust PK,PD data set should be a priority in early phase research; the focus of this article is to provide simple solutions and ideas that can help maximise the potential of industry sponsored clinical pharmacology studies, while helping close the gap between the desire to have a study meet its objectives and actually meeting those objectives.

As expected in the current environment of escalating costs and shrinking R&D budgets and staff, companies are looking to do more with less. The cost of conducting clinical pharmacology studies is often a key driver of many decisions regarding study planning and execution. Rightfully so, it makes sense that studies should be designed and executed to answer the most salient questions with minimal R&D spend while exposing a minimal number of subjects to study the drug. Furthermore, it makes sense to get the most information out of a clinical study to justify the time and money spent and the impact on the development programme. However, one specific factor that is often not considered is the ‘opportunity cost’ of lost or sub-optimal data. The opportunity cost of a sub-optimal data set can be quite high if, for example, a study needs to be repeated or clarity was not gained regarding dose selection for future studies. It’s important to keep in mind that even some of the smallest decisions made regarding the collection of PK, PD data may have a significant impact on the overall development programme.

STUDY PLANNING – SETTING UP THE STUDY FOR SUCCESS

Everything starts with an adequate study design and prospective planning for the inevitable, and often predictable, challenges No one sets out to conduct a clinical study that will not meet its objectives. However, all too often, studies fail to meet their objectives or fail to reach their full potential by generating a less than optimal data set. This is particularly pronounced with clinical pharmacology investigations that involve pharmacokinetic (PK) or pharmacodynamic (PD) analyses. A number of common pitfalls in the planning, conduct, and analysis of studies with a PK, PD component can result in a sub-optimal data set, which can then have a significant impact on future decisions within drug development programmes. Obtaining a robust PK,PD data set should be a priority in early phase research; the focus of this article is to provide simple solutions and ideas that can help maximise the potential of industry sponsored clinical pharmacology studies, while helping close the gap between the desire to have a study meet its objectives and actually meeting those objectives. associated with obtaining PK and PD data. Consideration should be given to the following concepts and examples when designing clinical studies with a PK, PD component.

Design a Rational PK, PD Sample Schedule

Designing a rational PK, PD sampling schedule based on nonclinical data, PK, PD modelling, or prior human data, is critical. This may seem like common sense, but it is frequently not a significant consideration and not optimised. A pharmacokineticst will be able to evaluate a particular drug and make recommendations regarding the starting sampling schedule. Keep in mind the word ‘starting’ when describing the sampling schedule, as it should rarely be considered a static entity (see comments in the Study Conduct section of the article). Furthermore, there is a fine line between obtaining too few samples and too many, but if there is one place not to cut costs it is related to having an adequate sampling schedule. Keeping subjects in a study for one or two more days beyond original expectations to collect additional PK samples could help better define the terminal phase and produce an overall better data set.

Sample Size Considerations – Make the Right Choice

Typically, clinical pharmacology studies are not powered to show statistically significant differences, except for certain studies (for instance, to demonstrate bioequivalence). The number of subjects needed in any given study is somewhat arbitrary and is driven, in part, by having sufficient numbers to adequately determine the PK, PD profile. As such, a common misconception is that sample size is not very important in clinical pharmacology studies, and all too often the number of subjects available for PK, PD analysis is too low. Reviewing regulatory guidance documents or consulting with an experienced pharmacokineticst for sample size considerations is very important.

Protocol Development – Slow Down

Don’t rush to complete protocols without significant prospective planning and in-depth discussions regarding design, conduct and analysis. Rushing this process will frequently result in a sub-optimal protocol and PK, PD data set. While slowing down is easier said than done, rushing protocol development frequently leads to important considerations being overlooked or inadvertent incorporation of errors that may translate into a missed opportunity to maximise a particular investigation. In my experience, there appears to be a strong relationship between the amount of time spent on study planning and the quality of the data obtained.

Parallel or Sequential Cohorts?

While dose escalation studies are run by enrolling sequential cohorts of subjects, other types of clinical pharmacology studies can be run in either a sequential or parallel fashion. While parallel cohorts offer attractive savings from an overall timeline perspective, there may be an opportunity cost associated with that approach. Consider that cohorts enrolled simultaneously do not allow for adjustment to the visit or PK, PD sampling schedule, which can translate into a missed opportunity to obtain a robust PK, PD data set. Running sequential cohorts leads to the ability to modify the protocol and study design for unexpected issues.

Inpatient versus Outpatient Studies?

There is a significant advantage to conducting a clinical pharmacology study at a commercial clinical research unit (CRU) due to the well-controlled and regimented environment to collect quality data. The disadvantage is that inpatient studies can be very expensive, especially for long-term studies. While conducting an outpatient study may be attractive from a cost perspective, the opportunity cost of missed or sub-optimal data is usually greater. Missed PK, PD sampling time points, undocumented concomitant medications, failure to take study drug and failure to return to the CRU for study visits are all variables that impact the quality of PK, PD data and, under most circumstances, outweigh the cost of conducting a strict inpatient study.

Don’t Cut Corners in Multiple Dose Studies

Ensure that a steady state is attainable and measurable in multiple dose studies by taking a cautious approach to the dosing and sampling schedule; a sufficiently long multiple dose period should be selected with an adequate number of trough samples collected leading up to the final dose. Each drug and study is different; consulting a pharmacokineticst for advice on the dosing and trough sampling schedule is important. Also, take care with multiple dose drug-drug interaction studies to ensure sufficient induction/inhibition of key enzymatic pathways. If a study’s objective revolves around reaching steady state, it is a simple matter of ensuring that steady state is attained and quantifiable.

A Pilot Food Effect Study

While a formal food effect study (appropriately powered) is recommended during the life cycle of most development programmes, a pilot food effect investigation as part of an early phase study can be useful. However, it is very important to design the pilot food effect study properly to maximise its potential. Here again sample size is important, not to establish statistical significance but to have adequate numbers of subjects to allow for sufficient PK, PD characterisation. Also, it is very important to use an intrasubject crossover design as opposed to an inter-subject noncrossover design. An inter-subject study design and/or too few subjects in a pilot food effect investigation has the potential to create confusion regarding administration of a drug with food.

Additional Items

Consider including an assessment of urine PK in early stage studies (such as first in human studies) and don’t start with a dose that is not expected to produce measurable plasma concentrations if toxicology or safety data allows for higher human starting dose levels.

A final point regarding study planning is to consider including placebo subjects in clinical pharmacology studies where appropriate. While this has no impact on the PK, PD data, it is simply another aspect of proper study planning that is commonly overlooked in early phase research. For example, placebo subjects may deflect concerns about the potential viability of drug if random adverse events or other safety signals appear during the course of a study.

STUDY CONDUCT – EXECUTING A WELL RUN STUDY

After the protocol is finalised and the CRU selected, decisions regarding adequate collection of PK, PD data and the potential for missed opportunities do not stop. Adequate planning and protocol development can only go so far. The next challenge is to execute the study in a manner that maximises its potential.

Assess the PK, PD Sampling Schedule during the Study

While designing the initial PK, PD sampling schedule is critical, it is generally based on logical guesses from nonclinical data or potentially sub-optimal human data. It’s not reasonable to expect that the starting PK, PD sampling schedule will be perfect for complete characterisation of the concentration versus time profile. However, far too often companies will consider sampling schedules to be static and complete a study without determining the adequacy of the data being collected. Whenever possible, analysing the concentration time profiles between dose escalation cohorts is critical. While analysing samples between cohorts may prolong the study and increase the cost, there is a significant opportunity cost associated with an inadequate characterisation of the PK, PD profile. For example, a poor plasma sampling schedule that does not define the terminal phase can result in sub-optimal estimation of PK parameters such as half-life. Conversely, sampling for longer than necessary (that is, sampling long after concentrations reach the lower limit of quantification) imparts higher study conduct costs and a greater burden on subjects for no gain.

Be Flexible During the Conduct of a Study

Early-phase human studies are typically initiated with limited or no prior human data and there is an expectation that these studies will be exploratory to some degree. Don’t take a rigid approach to these investigations and miss an opportunity to point the study in the right direction based on feedback from PK, PD analyses or other measured parameters. Flexibility could take the form of allowing for additional study visits to collect more PK, PD samples or obtaining more PD samples along different portions of the dose-response curve.

Protocol Amendments are Important Tools

A protocol amendment is generally viewed as a failure to properly plan a study from the beginning. However, protocol amendments are powerful tools in early phase research as they provide the flexibility to obtain robust data. For efficiency, consider having a protocol amendment ready for rapid IRB/EC submission based on anticipated challenges associated with the study.

Obtaining the Maximum Tolerated Dose A common objective of early phase studies is to establish the maximum tolerated dose (MTD) in humans. Cutting studies short before an MTD is reached is a common issue that has a significant opportunity cost associated with PK, PD data. Not reaching an MTD translates into less data and a less robust PK, PD data set, as well as having a downstream effect on not having an upper range of safety.

STUDY ANALYSIS

After conducting a well planned and executed study and generating a robust PK, PD data set, the final step is data analysis and determination of PK, PD parameters. This critical step serves to translate study results into useable information for subsequent studies in the development programme. Inadequate interpretation or analysis of the data is another potential opportunity cost that should be considered when identifying resources to perform the analysis. Reporting the results can be done in a standalone PK, PD report (attached as an appendix to the clinical study report) or as part of an integrated clinical/PK/PD report.

CONCLUSION

While it may be difficult during the planning, conduct and analysis of an early-phase clinical pharmacology study to see how individual decisions can affect future studies in an overall development programme, a general underlying theme guiding those decisions should be to maximise the potential for generating the best and most complete PK, PD data set. Seize the available opportunities to make the most of individual studies and set up your drug development programmes for success.


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Geoffrey Banks is President of ClinPharm Consulting, based in Research Triangle Park, North Carolina. Geoffrey completed his PhD at Washington State University and worked at the National Institutes of Health before transitioning to industry roles. Working with over 25 pharmaceutical/ biotech clients in his nearly 10-year career (from global leaders such as GSK to start-up companies), he has extensive experience with early phase drug development programmes. In 2008, Geoffrey founded ClinPharm Consulting, which focuses exclusively on clinical pharmacology and is positioning the firm as an industry leader in consulting business.
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