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Clinical Pharmacology & Pharmacometrics Should Collaborate to Implement Model-Informed Drug Development

Symbiosis between a modeler and a trialist is often required to develop a model-informed drug development strategy. This is because modelers can design an experiment the right way and allow for the results to be interpreted in a more integrative fashion. The modeler often views the totality of the data from multiple lines of investigation while the trialist is likely focused on the immediate trial.

The way that modeler and trialist roles are set up in any R&D organization often determines the seamlessness in the execution of such a strategy. In some organizations, these roles can be combined in a single function. But frequently, these are handled by two or more separate departments. For example, the trialist could be from a clinical pharmacology, experimental medicine, or clinical development function. Likewise, the modeler could be sourced from a quantitative science, data sciences, biostatistics, or pharmacometrics (PMX) function.

If you are a part of the latter structure, then this blog is likely of interest to you as we highlight 3 key areas where integrating modeling and trial science adds value.

#1 Understanding response and mechanism of drug action

This is quite an important area for impact where the two disciplines need to work together. Identifying what constitutes a drug response, how that is measured, and ensuring that the response being modeled is a direct measure of the pharmacology is a key consideration. In early clinical trials, investigators may observe many responses. They may range from biomarkers, mechanistic effect (e.g., enzyme inhibition), potential or accepted surrogate measures (e.g., blood pressure, lipoproteins, etc.), to clinically remote measures (e.g., receptor occupancy). Ensuring the right information is collected in the trials and understanding what putative value they have to overall biology is key.

Depending on the type of effect assessed, it will be helpful to consider whether the effect is derived from a single dose, at steady state, or at the end of the dosing interval. This will ensure the right modeling method is chosen. Depending on whether the effects are delayed or not as compared to exposure, there may be time dependencies that need to be assessed. Modeling can be used to interrogate the data to ensure that assumptions regarding the time course of the effect are valid.  A variety of other time-related effects on drug action such as induction, tolerance, and chronopharmacology could influence the way PMX analyses are set up. This is where a collaboration between clinical pharmacology and modeler is essential.

#2 Determining the optimal study design for the question at hand

Often, Sponsors conduct clinical studies using trial designs that are more “fit for purpose” and address the immediate issue at hand. The choice of study design could affect the way PMX analyses are set up. Before each study design discussion, the modeler and the trialist should collaborate to determine the optimal study design with the best operating characteristics that would optimally inform planned analyses. The FDA exposure/response guideline reflects nicely on such choices.


Table from the Exposure-Response (E-R) Guidance from the FDA explaining the points to consider in study design and E-R analysis for each study type
(Adapted from ER Guidance, FDA at URL: https://www.fda.gov/media/71277/download)

Another area for trialist-modeler collaboration is selection of dose and dosing regimen. Traditionally, dose selection was made by gestalt by the clinician. Often, this meant selecting a maximum tolerated or feasible dose, such that a single effect is elucidated. This is not a viable strategy from a “learn and confirm” perspective. Often, we need to understand the dynamic range of effects as a function of a wider range of doses levels. A modeler may suggest a more informative dose paradigm to identify the minimum effective dose or a dose that can yield desirable clinical outcomes or a range to interpolate a dose for further study. Discussing the method of dose selection is often valuable compared to deterministic dose selection.

In this context, there are a couple of other aspects worth noting. One relates to analytes measured and the other to the pharmacokinetic (PK) endpoints. Regarding analytes, ensure both parent and metabolites are measured in the clinical studies. Metabolites aren’t optional! Important considerations for metabolite assessments include whether they are active, toxic (in pre-clinical studies, in which case monitoring is essential), have downstream pharmacology for delayed effects, and whether they may explain altered pharmacodynamics. Increase your efficiency by ensuring metabolites are also part of the PMX analyses.

Another common area of consideration in any analysis includes the PK parameter of interest. Often, AUC, Cmax, and Cmin are pursued. When selecting an endpoint, identify what parameter best tracks with efficacy and safety. Remember some safety endpoints track better with Cmax (e.g., vital signs) and some better with AUC (e.g., hepatic enzymes).


A drug concentration-time curve that explains all the major pharmacokinetic parameters
Figure 1. Key PK parameters (source: https://clinicalinfo.hiv.gov/en/glossary/cmin)

#3 Assessing the clinical meaningfulness of a change

Some of the greatest confusion in drug development stems from deciding on a threshold for a clinically significant effect. A good understanding of the threshold for clinical significance of effect is necessary for any new molecule/biologic development program. This is key because of posology considerations but also package insert considerations. One normally starts with the easiest of available thresholds, the “bioequivalence bounds”. These are very stringent bounds (acceptance intervals of 0.8-1.25) used in formulation specifications studies where such stringency in product performance is expected.


Figure 2. Using confidence intervals to differentiate statistical significance from clinical importance. Source: Osuolale, Kazeem. (2020). Confidence Interval as a Better Alternative to P-Value for Clinical Significance. 47-51. 10.9790/5728-1603044751.

However, such narrow bounds are typically unnecessary for a safe and well tolerated drug. Health authorities are always open to use of wider bounds, if justified. Ensuring the understanding of the dose margins (e.g., clinical dose of 10 mg vs highest dose studied of 100 mg) and whether there are dose limiting safety aspects or saturable effects due to solubility of the drug substance would be essential. Modelers can help determine this threshold by several aggregate and population-based methods, including, but not limited to model-based meta-analysis (MBMA).

Related to this aspect is determining the worst-case effect for a given dose or dosing regimen. As an example, if a sponsor is developing a drug with a blood pressure adverse effect, one might ask, “what is the risk that the drug will result in x% high blood pressure?” or “what is the worst QTc effect that could occur with this drug?” This question and questions like these deserve the collective inputs of trialists and modelers. The underlying mechanism of effect and not just the effect itself (i.e., what you see) is important to understand. This is embodied in the extreme value theory that has been in use since the early 1900s. A good articulation of the use of extreme value theory to pharmacometrics is eloquently described in the work by Bonate, 2020. Therefore, any modeling analysis should characterize the variability in response and not just focus on characterizing mean trends.

In summary, executing on a model-informed drug development strategy requires a close connection and, ideally, a seamless integration of the trialist and modeler functions. They can collectively maximize the value harnessed from model-informed drug development approaches in the selection of the right dose, right study population, right endpoints, and right registration strategy.

For a brief primer of how MBMA can assist in new drug development, refer to the blog below.


References

  • Bonate P. The application of extreme value theory to pharmacometrics. Journal of Pharmacokinetics and Pharmacodynamics. URL: https://doi.org/10.1007/s10928-020-09721-0.
  • Osuolale, Kazeem. (2020). Confidence Interval as a Better Alternative to P-Value for Clinical Significance. 47-51. 10.9790/5728-1603044751.
  • US FDA Exposure-Response Guidance. URL: https://www.fda.gov/media/71277/download (Last accessed: September 16, 2022)

About the authors

Rajesh Krishna, PhD
By: Rajesh Krishna, PhD

Rajesh Krishna, PhD, FAAPS is Senior Director, clinical pharmacology and lead of the integrated practice area on rare diseases at Certara Strategic Consulting. With ~25 years of combined pharmaceutical industry and consulting experience, he has contributed to over 40 INDs; over 200 Phase 1/1b studies; and to several NDAs/BLAs. He is an author of Raj’s clinical pharmacology blogs.

Rik de Greef
By: Rik de Greef

Rik de Greef is a senior vice president of global quantitative science services at Certara. Rik was trained as a PK-PD scientist at Leiden University, The Netherlands.

Over the years, Rik has taken on roles with increasing responsibilities within Organon and its successor companies Schering-Plough and Merck/MSD. Most recently, has was site lead for Clinical PK-PD. In this role he has led the expansion of the group from 15 to 34 coworkers. Also, he has led the preparations of the early clinical components of the BLA submission for Merck’s key program in oncology, pembrolizumab.

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