Modeling and simulation can provide “meaningful prediction” of a drug’s pharmacodynamic range and maximum dose – the use of which might have prevented the tragic outcome of the BIAL 10-2474 trial in 2016, says Certara VP.
The European Medicines Agency’s (EMA’s) recently responded to Certara’s “Letter to the Editor,” both of which were published in Clinical Pharmacology & Therapeutics.
The EMA said it “welcomed the initiative shown” in Certara’s letter, which describes the role that quantitative systems pharmacology (QSP) can play in defining dosing criteria for first-in-human (FIH) clinical trials.
The agency said it encourages the use of mechanistic models to refine predictions from standard preclinical procedures as well as the use of additional drug-specific or mechanistic data or considerations. It continued: “Relevant models holding the potential to better reflect a substance’s effects in human tissues and potentially improve safety of trial participants will be supported by EMA.”
According to Certara, its QSP approach builds on the EMA FIH guidance and emphasizes “the better use of preclinical data to guide rational dose selection in FIH studies.”
The EMA guideline was issued in response to the 2016 BIAL 10-2474 FIH trial in which one patient died and five others were hospitalized. The adapted guideline stresses a sponsor’s responsibility to define the uncertainty associated with the medicines tested in clinical trials.
“In our view, use of our existing QSP model could have provided a more meaningful prediction of the pharmacodynamic range and maximum dose for the BIAL 10-2474 FIH trial than preclinical animal data and arguably might have prevented the disastrous outcome,” Piet van der Graaf, PharmD, PhD, Certara vice president, QSP, told us.
As to why similar technology was not used in the BIAL trial, van der Graaf believes it was mainly due to a lack of familiarity with QSP models among companies and regulators.
“Another factor is that not every company will have the resources and expertise to develop QSP models,” he added.
As van der Graaf explained, QSP models can complement conventional preclinical paradigms in translational drug research to guide FIH study design. “In particular, QSP models can be used to determine a safe and effective dose range and select the most informative biomarkers,” he said.
In addition to the scientific benefits, van der Graaf said using QSP also saves companies from spending time and resources running “low-value” preclinical studies.
He added, “Another benefit is that QSP models can integrate all the knowledge and data around a compound/mechanism of action and new information can be added on an ongoing basis during clinical development.”
A commitment to modeling and simulation
The US Food and Drug Administration (FDA) in 2017 outlined goals for improving the tools of clinical pharmacology, among them is “advancing quantitative systems pharmacology and building precompetitive models.”
“While already quite strong, both the FDA and EMA have ‘doubled down’ on their commitment to modeling and simulation recently,” said van der Graaf.
As part of this, van der Graaf noted that the EMA has converted its modeling and simulation working group to a temporary working party, “to reflect the greater role those approaches are expected to play in regulatory decision-making over the coming years.”
The FDA established its agency-wide group last year.
Additionally, FDA Commissioner Dr. Scott Gottlieb in a June blog post said: “…the more widespread use of modeling and simulation, the greater use of real-world evidence in the pre- and post-market setting, and the adoption of better tools for collecting and evaluating more real-time safety information after products are approved” are among the new scientific domains that have been introduced into the development and review process.